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DTSTART:19450814T190000
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END:VTIMEZONE
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20220930T204149Z
DESCRIPTION:Several fields of science are experiencing a massive growth in
the complexity and size of data being collected. In forestry\, ecology a
nd the environmental health sciences\, satellite images\, remotely sensed
data\, and cheap sensors such as air quality monitors are increasingly u
sed to understand the impacts of climate change and its impact on life on
earth. In these contexts\, Gaussian processes (GPs) can in principle hel
p answer many scientific questions\, especially when embedded in flexible
Bayesian hierarchical models with multivariate outcomes. However\, GPs p
erform poorly when challenged with massive datasets. To resolve these iss
ues\, I will introduce Meshed Gaussian Processes (MGPs) and the associate
d Markov-chain Monte Carlo (MCMC) algorithms. MGPs are a class of spatial
processes in which regions of a partitioned spatial domain are linked to
a patterned directed acyclic graph (DAG). These patterns\, introduced by
design\, lead to computational advantages. Specific applications motivat
e the use of special DAGs for building MGPs. In particular\, I will consi
der hypercube DAGs for satellite imaging data and treed DAGs for multivar
iate misaligned data. Finally\, I will introduce MCMC methods for more ch
allenging non-Gaussian data types and R package 'meshed' for Bayesian geo
statistics with multivariate multi-type spatial data.
DURATION:PT1H
DTSTAMP:20230105T051728Z
DTSTART;TZID=America/New_York:20230113T153000
LAST-MODIFIED:20230105T051728Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Make your own kind of sparse DAG\; Fit your own special scalable G
aussian process. Bayesian geostatistics for massive data
UID:CAL-8a0183a7-83184018-0185-783421c0-000033eddemobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=statsci-seminar-coordina
tor@duke.edu:Seminar Coordinator
X-BEDEWORK-SPEAKER:Dr. Michele Peruzzi\, Postdoctoral Associate\, Duke Sta
tistical Science
X-BEDEWORK-DUKE-SERIES:Statistical Science Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20220930T204149Z
DESCRIPTION:Variables contained within the global oceans can detect and re
veal the effects of the warming climate as the oceans absorb huge amounts
of solar energy. Hence\, information regarding the joint spatial distrib
ution of ocean variables is critical for climate monitoring. In this pape
r\, we investigate the spatial correlation structure between ocean temper
ature and salinity using data harvested from the Argo program and constru
ct a model to capture their bivariate spatial dependence from the surface
to the ocean's interior. We develop a flexible class of multivariate non
stationary covariance models defined in 3-dimensional (3D) space (longitu
de x latitude x depth) that allows for the variances and correlation to c
hange along the vertical pressure dimension. These models are able to des
cribe the joint spatial distribution of the two variables while incorpora
ting the underlying vertical structure of the ocean. We demonstrate that
the proposed cross-covariance models describe the complex vertical cross-
covariance structure well\, while existing cross-covariance models includ
ing bivariate Matérn models poorly fit empirical cross-covariance structu
re. Furthermore\, the results show that using one more variable significa
ntly enhances the prediction of the other variable and that the estimated
spatial dependence structures are consistent with the ocean stratificati
on.
DURATION:PT1H
DTSTAMP:20230105T173232Z
DTSTART;TZID=America/New_York:20230118T153000
LAST-MODIFIED:20230105T173232Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:3D Bivariate Spatial Modelling of Argo Ocean Temperature and Salin
ity Profiles
UID:CAL-8a0183a7-83184018-0185-7d934610-00001ab7demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=statsci-seminar-coordina
tor@duke.edu:Seminar Coordinator
X-BEDEWORK-SPEAKER:Dr. Mary Salvaña\, Statistician / Postdoctoral Research
Fellow\, University of Houston
X-BEDEWORK-DUKE-SERIES:Statistical Science Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20220930T204149Z
DESCRIPTION:In this talk I will give an overview of my two main lines of r
esearch\, Bayesian nonparametric modelling and theory of Bayesian computa
tion\, by discussing hierarchical models\, widely applied probabilistic s
tructures that allow to borrow information among distinct groups.\nPartic
ular emphasis will be given on the inferential and computational implicat
ions of this specification\, starting from applied examples.\n\nIn the se
cond part of the talk I will focus on the study of Gibbs samplers\, which
are popular algorithms to approximate posterior distributions arising fr
om Bayesian models. Despite their popularity and good empirical performan
ces\, however\, there are still relatively few quantitative theoretical r
esults on their scalability or lack thereof\, e.g. much less than for gra
dient-based sampling methods. In a work with Giacomo Zanella (Bocconi Uni
versity)\, we introduce a novel technique to analyse the asymptotic behav
iour of mixing times of Gibbs Samplers\, based on tools of Bayesian asymp
totics. Our methodology applies to high-dimensional regimes where both nu
mber of datapoints and parameters increase\, under random data-generating
assumptions. The framework is applied to two-level hierarchical models w
ith generic likelihoods and exponential family priors. In this context we
are able to provide dimension-free convergence results for Gibbs Sampler
s under mild conditions.
DURATION:PT1H
DTSTAMP:20230105T221735Z
DTSTART;TZID=America/New_York:20230120T153000
LAST-MODIFIED:20230105T221735Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Hierarchical Structures in Bayesian Statistics
UID:CAL-8a0183a7-83184018-0185-840174c5-00001592demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=statsci-seminar-coordina
tor@duke.edu:Seminar Coordinator
X-BEDEWORK-SPEAKER:Filippo Ascolani\, PhD student\, Statistics\, Bocconi U
niversity\,
X-BEDEWORK-DUKE-SERIES:Statistical Science Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20220930T204149Z
DESCRIPTION:We develop the sparse VAE for unsupervised representation lear
ning on high-dimensional data. The sparse VAE learns a set of latent fact
ors (representations) which summarize the associations in the observed da
ta features. The underlying model is sparse in that each observed feature
(i.e. each dimension of the data) depends on a small subset of the laten
t factors. As examples\, in ratings data each movie is only described by
a few genres\; in text data each word is only applicable to a few topics\
; in genomics\, each gene is active in only a few biological processes. W
e prove such sparse deep generative models are identifiable: with infinit
e data\, the true model parameters can be learned. (In contrast\, most de
ep generative models are not identifiable.) We empirically study the spar
se VAE with both simulated and real data. We find that it recovers meanin
gful latent factors and has smaller heldout reconstruction error than rel
ated methods.
DURATION:PT1H
DTSTAMP:20230105T051534Z
DTSTART;TZID=America/New_York:20230125T153000
LAST-MODIFIED:20230105T051534Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Identifiable Deep Generative Models via Sparse Decoding
UID:CAL-8a0183a7-83184018-0185-805b2331-000062f0demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=statsci-seminar-coordina
tor@duke.edu:Seminar Coordinator
X-BEDEWORK-SPEAKER:Dr. Gemma Moran\, Postdoctoral Associate\, Columbia Dat
a Science Institute
X-BEDEWORK-DUKE-SERIES:Statistical Science Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20220930T204149Z
DESCRIPTION:Randomized experiments allow for consistent estimation of the
average treatment effect based on the difference in mean outcomes without
strong modeling assumptions. Appropriate use of pretreatment covariates
can further improve the estimation efficiency. Missingness in covariates
is nevertheless common in practice and raises an important question: shou
ld we adjust for covariates subject to missingness\, and if so\, how? The
unadjusted difference in means is always unbiased. The complete-covariat
e analysis adjusts for all completely observed covariates and is asymptot
ically more efficient than the difference in means if at least one comple
tely observed covariate is predictive of the outcome. Then what is the ad
ditional gain of adjusting for covariates subject to missingness? To reco
ncile the conflicting recommendations in the literature\, we analyze and
compare five strategies for handling missing covariates in randomized exp
eriments under the design-based framework\, and recommend the missingness
-indicator method\, as a known but not so popular strategy in the literat
ure\, due to its multiple advantages. First\, it removes the dependence o
f the regression-adjusted estimators on the imputed values for the missin
g covariates. Second\, it does not require modeling the missingness mecha
nism\, and yields consistent estimators even when the missingness mechani
sm is related to the missing covariates and unobservable potential outcom
es. Third\, it ensures large-sample efficiency over the complete-covariat
e analysis and the analysis based on only the imputed covariates. Lastly\
, it is easy to implement via least squares. We also propose modification
s to it based on asymptotic and finite sample considerations. Importantly
\, our theory views randomization as the basis for inference\, and does n
ot impose any modeling assumptions on the data generating process or miss
ingness mechanism.
DURATION:PT1H
DTSTAMP:20230109T212525Z
DTSTART;TZID=America/New_York:20230127T153000
LAST-MODIFIED:20230109T212525Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:To Adjust or not to Adjust? Estimating the Average Treatment Effec
t in Randomized Experiments with Missing Covariates
UID:CAL-8a0183a7-83184018-0185-986c823f-000026b4demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=statsci-seminar-coordina
tor@duke.edu:Seminar Coordinator
X-BEDEWORK-SPEAKER:Dr. Anqi Zhao\, Assistant Professor\, National Universi
ty of Singapore
X-BEDEWORK-DUKE-SERIES:Statistical Science Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20220930T204149Z
DESCRIPTION:This week's department seminar will feature talks from Statist
ical Science undergraduate students involved in research. Each of the und
ergraduate speakers will have 5-7 minutes to present their research\, fol
lowed by a brief Q&A. A reception to celebrate our Statistical Science un
dergraduate researchers and their advisors will follow. Come see the vari
ety of work being done by undergraduate researchers in our department!
DURATION:PT1H
DTSTAMP:20230209T055713Z
DTSTART;TZID=America/New_York:20230210T153000
LAST-MODIFIED:20230209T055713Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Undergrads Take Over StatSci Seminar!
UID:CAL-8a0183a7-83184018-0183-902248a0-00001691demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=joan.durso@duke.edu:Joan
Durso
X-BEDEWORK-SPEAKER:Statistical Science Undergraduate Students
X-BEDEWORK-DUKE-SERIES:Statistical Science Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Business
CATEGORIES:Other
CATEGORIES:Utilities
CATEGORIES:Information Session
CATEGORIES:Main
CATEGORIES:Student
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20230123T151610Z
DESCRIPTION:Harvard Business School information session. Learn more about
the MBA programs and application process.
DURATION:PT1H30M
DTSTAMP:20230126T101250Z
DTSTART;TZID=America/New_York:20230301T190000
LAST-MODIFIED:20230126T101250Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Harvard Business School - Information Session (MBA and 2+2 Program
)
UID:CAL-8a0290cd-85a68f68-0185-df33783d-00004d5ddemobedework@mysite.edu
X-BEDEWORK-SUBMIT-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Information Session:/
public/aliases/Other/Information Session
X-BEDEWORK-SUBMIT-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Business:/public/alia
ses/Topics/Business
X-BEDEWORK-CONTACT:Academic Deans\, Trinity College of Arts &\; Science
s
X-BEDEWORK-SUBMIT-COMMENT:
X-BEDEWORK-SUBMITTER-EMAIL:ldb30@duke.edu
X-BEDEWORK-DUKE-SPONSOR:/principals/users/agrp__ArtsandSciences_TrinityCol
lege
X-BEDEWORK-SUBMITTEDBY:ldb30
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Information Session:/user/pu
blic-user/Other/Information Session
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Business:/user/public-user/T
opics/Business
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Student:/user/public-user/Ut
ilities/Student
X-BEDEWORK-STUDENT-CONTACT:Academic Deans\, Trinity College of Arts & Scie
nces
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20220930T204149Z
DESCRIPTION:To select outcomes for clinical trials testing experimental th
erapies for Huntington disease\, a fatal neurodegenerative disorder\, ana
lysts model how potential outcomes change over time. Yet\, subjects with
Huntington disease are often observed at different levels of disease prog
ression. To account for these differences\, analysts include time to clin
ical diagnosis as a covariate when modeling potential outcomes\, but this
covariate is often censored. One popular solution is imputation\, whereb
y we impute censored values using predictions from a model of the censore
d covariate given other data\, then analyze the imputed dataset. However\
, when this imputation model is misspecified\, our outcome model estimate
s can be biased. To address this problem\, we developed a novel method\,
dubbed ``ACE imputation.'' First\, we model imputed values as error-prone
versions of the true covariate values. Then\, we correct for these error
s using semiparametric theory. Specifically\, we derive an outcome model
estimator that is consistent\, even when the censored covariate is impute
d using a misspecified imputation model. Simulation results show that ACE
imputation remains empirically unbiased even if the imputation model is
misspecified\, unlike multiple imputation which yields $>100\\%$ bias. Ap
plying our method to a Huntington disease study pinpoints outcomes for cl
inical trials aimed at slowing disease progression.
DURATION:PT1H
DTSTAMP:20230222T183048Z
DTSTART;TZID=America/New_York:20230310T153000
LAST-MODIFIED:20230222T183048Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Mission Imputable: Correcting for Berkson Error When Imputing a Ce
nsored Covariate
UID:CAL-8a0290b4-860465b2-0186-7a64719a-000053b5demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=statsci-seminar-coordina
tor@duke.edu:Seminar Coordinator
X-BEDEWORK-SPEAKER:Tanya P. Garcia\, Associate Professor of Biostatistics\
, UNC-Chapel Hill\, Gillings School of Public Health
X-BEDEWORK-DUKE-SERIES:Statistical Science Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Utilities
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20230320T161313Z
DESCRIPTION:Randomized experiments are the gold standard for inferring a c
ausal effect. Consequently\, many organizations run thousands of randomiz
ed experiments to quantify the impact of product changes\, which managers
then use to inform deployment and investment decisions. Often\, these ex
periments are conducted on customers arriving sequentially\; however\, th
e analysis is only performed at the end of the study. This is undesirable
because large effects can be detected before the end of the study\, whic
h is especially important if the treatment effect is negative. Alternativ
ely\, analysts could perform hypotheses tests more frequently and stop th
e experiment when the estimated causal effect is statistically significan
t\; this practice is often called ``peeking.'' Unfortunately\, peeking in
validates the statistical guarantees and an increased type-1 error. Our p
aper provides valid design-based confidence sequences\, sequences of conf
idence intervals with uniform type-1 error guarantees over time for vario
us sequential experiments in an assumption-light manner. In particular\,
our results apply to the average treatment effect for different individua
ls arriving sequentially\, the mean reward difference in multi-arm bandit
settings with adaptive treatment assignments\, the contemporaneous treat
ment effect for single time series experiment with carryover effects\, an
d the average contemporaneous treatment effect in panel experiments. We f
urther provide a variance reduction technique incorporating modeling assu
mptions and covariates to reduce the confidence sequence width proportion
al to how well we can predict the next outcome. Our work constructs both
exact and asymptotic design-based confidence sequences\; however\, our ma
in results focus on the asymptotic regime because of its general applicab
ility and attractive properties.
DURATION:PT1H
DTSTAMP:20230331T183713Z
DTSTART;TZID=America/New_York:20230331T153000
LAST-MODIFIED:20230331T183713Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Design-Based Anytime-Valid Causal Inference
UID:CAL-8a0290b4-860465b2-0186-ffcbd333-00007db3demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-SPEAKER:Iavor Bojinov\, Assistant Professor\, Business Administ
ration\, Harvard Business School
X-BEDEWORK-DUKE-SERIES:StatSci Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu
:Karen Whitesell
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Utilities
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20230320T161608Z
DESCRIPTION:As a computational alternative to Markov chain Monte Carlo app
roaches\, variational inference (VI) is becoming increasingly popular for
approximating intractable posterior distributions in large-scale Bayesia
n models due to its comparable efficacy and superior efficiency. Several
recent works provide theoretical justifications of VI by proving its stat
istical optimality for parameter estimation under various settings\; mean
while\, formal analysis on the algorithmic convergence aspects of VI is s
till largely lacking. In this talk\, we will discuss some recent advances
towards studying convergence of the popular coordinate ascent variationa
l inference algorithm. We will present some specific case studies and pro
ceed to develop a general framework for studying such questions.
DURATION:PT1H
DTSTAMP:20230331T183741Z
DTSTART;TZID=America/New_York:20230407T153000
LAST-MODIFIED:20230331T183741Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:On the Convergence of Coordinate Ascent Variational Inference
UID:CAL-8a0290b4-860465b2-0186-ffce7eab-00007ed3demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-SPEAKER:Anirban Bhattacharya\, Professor\, Department of Statis
tics\, Texas A&M University
X-BEDEWORK-DUKE-SERIES:StatSci Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu
:Karen Whitesell
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Utilities
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20230320T161608Z
DESCRIPTION:A broad class of regression models that routinely appear in se
veral fields of application can be expressed as partially or fully discre
tized Gaussian linear regressions. Besides incorporating the classical Ga
ussian response setting\, this class crucially encompasses probit\, multi
nomial probit and tobit models\, among others\, and further includes popu
lar extensions of such formulations to multivariate\, non-linear and dyna
mic contexts. The relevance of these representations has motivated decade
s of active research within the Bayesian field. A main reason for this co
nstant interest is that\, unlike for the Gaussian response setting\, the
posterior distributions induced by these models do not seem to belong to
a known and tractable class\, under the commonly-assumed Gaussian priors.
This has led to the development of several alternative solutions for pos
terior inference relying either on sampling-based methods or on determini
stic approximations\, that often experience scalability\, mixing and accu
racy issues\, especially in high dimension. In this seminar\, I will revi
ew\, unify and extend recent advances in Bayesian inference and computati
on for such a class of models\, proving that unified skew-normal (SUN) di
stributions (which include Gaussians as a special case) are conjugate to
the general form of the likelihood induced by these formulations. This re
sult opens new avenues for improved posterior inference\, under a broad c
lass of widely-implemented models\, via novel closed-form expressions\, t
ractable Monte Carlo methods based on i.i.d. samples from the exact SUN p
osterior\, and more accurate and scalable approximations from variational
Bayes and expectation-propagation. These results will be further extende
d\, in asymptotic regimes\, to the whole class of Bayesian parametric mod
els via novel limiting approximations relying on generalized skew-normal
distributions.
DURATION:PT1H
DTSTAMP:20230407T190842Z
DTSTART;TZID=America/New_York:20230414T153000
LAST-MODIFIED:20230407T190842Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:The role of skewed distributions in Bayesian inference: conjugacy\
, scalable approximations and asymptotics
UID:CAL-8a0182b3-870a191e-0187-5d14a8db-00004af1demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu
:Karen Whitesell
X-BEDEWORK-SPEAKER:Daniele Durante\, Assistant Professor\, Bocconi Univers
ity
X-BEDEWORK-DUKE-SERIES:StatSci Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Utilities
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20230320T161608Z
DESCRIPTION:Discrete random probability measures stand out as effective to
ols for Bayesian clustering. The investigation in the area has been very
lively\, with a strong emphasis on nonparametric procedures based on eith
er the Dirichlet process or on more flexible generalizations\, such as th
e normalized random measures with independent increments (NRMI). The lite
rature on finite-dimensional discrete priors is much more limited and mos
tly confined to the standard Dirichlet-multinomial model. While such a sp
ecification may be attractive due to conjugacy\, it suffers from consider
able limitations when it comes to addressing clustering problems. In orde
r to overcome these\, we introduce a novel class of priors that arise as
the hierarchical compositions of finite-dimensional random discrete struc
tures. Despite the analytical hurdles such a construction entails\, we ar
e able to characterize the induced random partition and determine explici
t expressions of the associated urn scheme and of the posterior distribut
ion. A detailed comparison with (infinite-dimensional) NRMIs is also prov
ided: indeed\, informative bounds for the discrepancy between the partiti
on laws are obtained. Finally\, the performance of our proposal over exis
ting methods is assessed on a real application where we study a publicly
available dataset from the Italian education system comprising the scores
of a mandatory nationwide test.
DURATION:PT1H
DTSTAMP:20230419T025126Z
DTSTART;TZID=America/New_York:20230421T153000
LAST-MODIFIED:20230419T025126Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Finite-dimensional discrete random structures and Bayesian cluster
ing
UID:CAL-8a0182b3-870a191e-0187-95f55e32-00007e8bdemobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu
:Karen Whitesell
X-BEDEWORK-SPEAKER:Tomasso Rigon\, Assistant Professor\, University of Mil
ano Bicocca
X-BEDEWORK-DUKE-SERIES:StatSci Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Utilities
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20230320T161608Z
DESCRIPTION:Geographical and two-dimensional regression discontinuity desi
gns (RDDs) extend the classic\, univariate RDD to multivariate\, spatial
contexts. We propose a framework for analyzing such designs with Gaussian
process regression. This yields a Bayesian posterior distribution of the
treatment effect at every point along the border\, allowing for impact h
eterogeneity. We can then aggregate along the border to obtain an overall
local average treatment effect (LATE) estimate. We address nuances of ha
ving a functional estimand defined on a border with potentially intricate
topology\, particularly with respect to even defining the target estiman
d of interest. The Bayesian estimate of the LATE can also be used as a te
st statistic in a hypothesis test with good frequentist properties\, whic
h we validate using simulations and placebo tests. We demonstrate our met
hodology with a dataset of property sales in New York City\, to assess wh
ether there is a discontinuity in housing prices at the border between sc
hool district. We also discuss application of this method to the context
of treatment as a function of two forcing variables\, such as falling bel
ow a threshold for either a reading or math test.\n\nJoint with Lily An\,
Zach Branson\, Maxime Rischard\, and Luke Bornn
DURATION:PT1H
DTSTAMP:20230420T185824Z
DTSTART;TZID=America/New_York:20230428T153000
LAST-MODIFIED:20230420T185824Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:A Bayesian Nonparametric Approach to Geographic and Two-Dimensiona
l Regression Discontinuity Designs
UID:CAL-8a0182b3-870a191e-0187-9ff58db7-00003e69demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu
:Karen Whitesell
X-BEDEWORK-SPEAKER:Luke Miratrix\, Associate Professor\, Harvard Universit
y
X-BEDEWORK-DUKE-SERIES:StatSci Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Other
CATEGORIES:Utilities
CATEGORIES:Meeting
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=18832e99-2a84ac8f-012a-85e0def0-00000038:Rauch\, Lo
ri
CREATED:20230605T143339Z
DESCRIPTION:The Statistical Science Department encourages all to attend th
e defense of this dissertation.
DURATION:PT2H
DTSTAMP:20230605T143339Z
DTSTART;TZID=America/New_York:20230620T083000
LAST-MODIFIED:20230605T143339Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Ecological Modeling via Bayesian Nonparametric Species Sampling Pr
iors
UID:CAL-8a018cb3-8855248a-0188-8bfa595f-00000f4bdemobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Meeting:/user/public-user/Ot
her/Meeting
X-BEDEWORK-SPEAKER:Alessandro Zito
X-BEDEWORK-SUBMITTEDBY:lr3 for Statistical Science (agrp_StatisticalScienc
e)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20230320T161608Z
DESCRIPTION:Bayesian deep Gaussian processes (DGPs) outperform ordinary GP
s as surrogate models of complex computer experiments when response surfa
ce dynamics are non-stationary\, which is especially prevalent in aerospa
ce simulations. Yet DGP surrogates have not been deployed for the canoni
cal downstream task in that setting: reliability analysis through contour
location (CL). Level sets separating passable vs. failable operating co
nditions are best learned through strategic sequential design. There are
two limitations to modern CL methodology which hinder DGP integration in
this setting. First\, derivative-based optimization underlying acquisit
ion functions is thwarted by sampling-based Bayesian (i.e.\, MCMC) infere
nce\, which is essential for DGP posterior integration. Second\, canonic
al acquisition criteria\, such as entropy\, are famously myopic to the ex
tent that optimization may even be undesirable. Here we tackle both of t
hese limitations at once\, proposing a hybrid criteria that explores alon
g the Pareto front of entropy and (predictive) uncertainty\, requiring ev
aluation only at strategically located "triangulation" candidates. We sh
owcase DGP CL performance in several synthetic benchmark exercises and on
a real-world RAE-2822 transonic airfoil simulation.
DURATION:PT1H
DTSTAMP:20230829T135017Z
DTSTART;TZID=America/New_York:20230901T153000
LAST-MODIFIED:20230829T135017Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Contour Location for Airfoil Simulation Experiments Using Deep Gau
ssian Processes
UID:CAL-8a02906b-8a0a73d8-018a-418f325f-00004b5ademobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=stat:Karen Whitesell
X-BEDEWORK-SPEAKER:Annie Booth\, Assistant Professor\, North Carolina Stat
e University
X-BEDEWORK-DUKE-SERIES:StatSci Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20230320T161608Z
DESCRIPTION:We study multiple testing in the normal means problem with est
imated\nvariances that are shrunk through empirical Bayes methods. The si
tuation is asymmetric in\nthat a prior is posited for the nuisance parame
ters (variances) but not the primary\nparameters (means).\nIf the prior w
ere known\, one could proceed by computing p-values\nconditional on sampl
e variances\; a strategy called partially Bayes inference by Sir David\nC
ox. These conditional p-values satisfy a Tweedie-type formula and are app
roximated at\nnearly-parametric rates when the prior is estimated by nonp
arametric maximum likelihood. If\nthe variances are in fact fixed\, the a
pproach retains type-I error guarantees. As is common\nin the empirical B
ayes paradigm\, our results hinge on the interpretation of the prior as t
he\nfrequency distribution of the nuisance parameters\, and should be con
trasted with e.g.\, the\nconditional predictive p-values of Bayarri and B
erger.\n\nBased on joint work with Bodhisattva Sen.
DURATION:PT1H
DTSTAMP:20230905T124056Z
DTSTART;TZID=America/New_York:20230908T153000
LAST-MODIFIED:20230905T124056Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Empirical partially Bayes multiple testing and compound χ² decisio
ns
UID:CAL-8a02906b-8a0a73d8-018a-655a1575-00004651demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=stat:Karen Whitesell
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-SPEAKER:Nikolaos (Nikos) Ignatiadis\, Assistant Professor\, The
University of Chicago
X-BEDEWORK-DUKE-SERIES:StatSci Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Sustainability
CATEGORIES:Ethics
CATEGORIES:Human Rights
CATEGORIES:Civic Engagement/Social Action
CATEGORIES:Global
CATEGORIES:Other
CATEGORIES:Utilities
CATEGORIES:Information Session
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20230830T160124Z
DESCRIPTION:Join us for an overview of DukeEngage and the application proc
ess\, plus a chance to hear from previous participants!
DURATION:PT1H
DTSTAMP:20230830T160201Z
DTSTART;TZID=America/New_York:20230914T163000
LAST-MODIFIED:20230830T160201Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:DukeEngage Info Session
UID:CAL-8a02906b-8a0a73d8-018a-472e27fe-00003993demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Information Session:/user/pu
blic-user/Other/Information Session
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Global:/user/public-user/Top
ic of Event Focused on a Country or Continent (if applicable)/Global
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Civic Engagement/Social Acti
on:/user/public-user/Topics/Civic Engagement_Social Action
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Ethics:/user/public-user/Top
ics/Ethics
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Human Rights:/user/public-us
er/Topics/Human Rights
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Sustainability:/user/public-
user/Topics/Sustainability
X-BEDEWORK-CS;X-BEDEWORK-PARAM-DESCRIPTION="/principals/users/agrp_DukeEng
age,":DukeEngage
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=aaron.crouse@duke.edu:Aa
ron Crouse
X-BEDEWORK-IMAGE-X1:0
X-BEDEWORK-IMAGE-Y1:0
X-BEDEWORK-IMAGE-X2:1059
X-BEDEWORK-IMAGE-Y2:706
X-BEDEWORK-IMAGE-CROP-WIDTH:1059
X-BEDEWORK-IMAGE-CROP-HEIGHT:706
X-BEDEWORK-IMAGE-ALT-TEXT:DukeEngage Info Sessions
X-BEDEWORK-SUBMITTEDBY:sr14 for Kenan Institute for Ethics (agrp_KenanInst
itute)
X-BEDEWORK-IMAGE:/public/Images/2023 DukeEngage Info Sessions DukeCal_2023
0830040124PM.png
X-BEDEWORK-THUMB-IMAGE:/public/Images/2023 DukeEngage Info Sessions DukeCa
l_20230830040124PM-thumb.png
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20230320T161608Z
DESCRIPTION:This talk will delve into two major causal inference obstacles
: (1) identifying which variables to account for and (2) assessing the im
pact of unmeasured variables. The first half of the talk will showcase a
Causal Quartet. In the spirit of Anscombe's Quartet\, this is a set of fo
ur datasets with identical statistical properties\, yet different true ca
usal effects due to differing data generating mechanisms. These simple da
tasets provide a straightforward example for statisticians to point to wh
en explaining these concepts to collaborators and students. The second ha
lf of the talk will focus on how statistical techniques can be leveraged
to examine the impact of a potential unmeasured confounder. We will exami
ne sensitivity analyses under several scenarios with varying levels of in
formation about potential unmeasured confounders\, introducing the tipr R
package\, which provides tools for conducting sensitivity analyses in a
flexible and accessible manner.
DURATION:PT1H
DTSTAMP:20230911T161935Z
DTSTART;TZID=America/New_York:20230915T153000
LAST-MODIFIED:20230911T161935Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Causal Quartet: When statistics alone do not tell the full story
UID:CAL-8a02906b-8a0a73d8-018a-84f81761-00005d37demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=stat:Karen Whitesell
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-SPEAKER:Lucy D'Agostino McGowan\, Assistant Professor\, Wake Fo
rest University
X-BEDEWORK-DUKE-SERIES:StatSci Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Other
CATEGORIES:Utilities
CATEGORIES:Information Session
CATEGORIES:Main
CATEGORIES:Student
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
X-BEDEWORK-COST:NA
CREATED:20230824T154441Z
DESCRIPTION:Harvard Business School - Information Session (Deferred MBA\,
2+2 Program).
DURATION:PT1H30M
DTSTAMP:20230825T132537Z
DTSTART;TZID=America/New_York:20230920T190000
LAST-MODIFIED:20230825T132537Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Harvard Business School - Information Session (Deferred MBA\, 2+2
Program)
UID:CAL-8a02906b-8a0a73d8-018a-28381ec9-00005740demobedework@mysite.edu
X-BEDEWORK-SUBMIT-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Information Session:/
public/aliases/Other/Information Session
X-BEDEWORK-CONTACT:Trinity Academic Deans
X-BEDEWORK-SUBMIT-COMMENT:
X-BEDEWORK-SUBMITTER-EMAIL:ldb30@duke.edu
X-BEDEWORK-DUKE-SPONSOR:/principals/users/agrp__ArtsandSciences_TrinityCol
lege
X-BEDEWORK-SUBMITTEDBY:ldb30
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Information Session:/user/pu
blic-user/Other/Information Session
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Student:/user/public-user/Ut
ilities/Student
X-BEDEWORK-STUDENT-CONTACT:Trinity Academic Deans
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20230320T161608Z
DESCRIPTION:Multivariate linear regression and randomization-based inferen
ce are \ntwo essential methods in statistics and econometrics. Neverthele
ss\,\nthe problem of producing a randomized test for the value of a singl
e\nregression coefficient that is exactly valid when errors are exchangea
ble\,\nand which is asymptotically valid for the best linear predictor\,
has\nremained elusive. In this paper\, we produce a test that is exactly\
nvalid with exchangeable errors and which allows for general covariate\nd
esigns\; covariates may be continuous as well as discrete\, and may be\nc
orrelated. The test is asymptotically valid when the errors are not\nexch
angeable\, in particular in the presence of conditional heteroskedasticit
y.
DURATION:PT1H
DTSTAMP:20230919T153025Z
DTSTART;TZID=America/New_York:20230922T153000
LAST-MODIFIED:20230919T153025Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:An Exact t-Test
UID:CAL-8a03932d-8a96243a-018a-ae1067bc-00007bc6demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=stat:Karen Whitesell
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-SPEAKER:Guillaume Pouliot\, University of Chicago
X-BEDEWORK-DUKE-SERIES:StatSci Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20230320T161608Z
DESCRIPTION:Generalized linear mixed models are the workhorse of applied S
tatistics. In modern applications\, from political science to electronic
marketing\, it is common to have categorical factors with large number of
levels. This arises naturally when considering interaction terms in surv
ey-type data\, or in recommender-system type of applications. In such con
texts it is important to have a scalable computational framework\, that i
s one whose complexity scales linearly with the number of observations $n
$ and parameters $p$ in the model. Popular implementations\, such as thos
e in lmer\, although highly optimized they involve costs that scale polyn
omially with $n$ and $p$. We adopt a Bayesian approach (although the esse
nce of our arguments applies more generally) for inference in such contex
ts and design families of variational approximations for approximate Baye
sian inference with provable scalability. We also provide guarrantees for
the resultant approximation error and in fact link that to the rate of c
onvergence of the numerical schemes used to obtain the variational approx
imation.\nThis is joint work with Giacomo Zanella (Bocconi) and Max Gople
rud (Pittsburgh)
DURATION:PT1H
DTSTAMP:20230927T222713Z
DTSTART;TZID=America/New_York:20230929T153000
LAST-MODIFIED:20230927T222713Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Accurate and scalable large-scale variational inference for mixed
models
UID:CAL-8a03932d-8a96243a-018a-cc5965ce-00001951demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu
:Karen Whitesell
X-BEDEWORK-SPEAKER:Omiros Papaspiliopoulos\, Professor\, Bocconi Universit
y
X-BEDEWORK-DUKE-SERIES:StatSci Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20230320T161608Z
DESCRIPTION:To sanitize data for the purpose of disclosure control is to d
estroy its precision in some way. When done in an explicit or controlled
manner\, the imprecision can be salvaged to the statistician's benefit. T
his talk discusses how imprecision that results from privacy protection m
ay be appropriated to improve our statistical understanding from the data
at hand. Two ideas are sketched. The first demonstrates how knowledge ab
out the imprecision can be harnessed to facilitate statistical computatio
n and recover inference in a manner faithful to the downstream task. The
second employs the imprecise probabilities vocabulary to establish analyt
ical limits for key inferential quantities under minimal knowledge or ass
umptions about the downstream task and the privacy mechanism. Both ideas
serve as persuasive arguments for a formal and transparent approach to di
sclosure control. \n\nThis body of work bears witness to the challenges t
hat emerged from the U.S. Census Bureau's revamp of its disclosure avoida
nce system for the 2020 Decennial Census\, and more broadly through effor
ts to expand data access to support research and policymaking under moder
n data governance directives. To that end\, I conclude with an assessment
of strongly quantitative notions of privacy\, notably differential priva
cy\, against prevailing qualitative guidelines of confidentiality protect
ion to highlight its benefits and limitations.
DURATION:PT1H
DTSTAMP:20230927T222737Z
DTSTART;TZID=America/New_York:20231006T153000
LAST-MODIFIED:20230927T222737Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:When a little imprecision can help: Case studies from statistical
privacy
UID:CAL-8a03932d-8a96243a-018a-d8ab0463-00006c13demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu
:Karen Whitesell
X-BEDEWORK-SPEAKER:Ruobin Gong\, Assistant Professor\, Rutgers
X-BEDEWORK-DUKE-SERIES:StatSci Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20230320T161608Z
DESCRIPTION:The tremendous increase in computation capabilities of edge de
vices\, along with the rapid market infiltration of powerful AI chips\, h
as led to explosive interest in collaborative analytics\, such as federat
ed learning\, that distribute model learning across diverse sources to pr
ocess more of the user's data at the origin of creation. To date\, these
efforts have focused mainly on predictive modeling\, where the goal is t
o create a global or personalized predictive map (often a deep network) t
hat leverages knowledge from different sources while circumventing the ne
ed to share raw data. In this talk\, I argue that predictive modeling\,
without untangling the nature of heterogeneity across users\, can lead to
swift and evident failures. With this in mind\, I then present: i) A des
criptive framework capable of extracting interpretable and identifiable f
eatures that describe what is shared and unique across diverse data datas
ets\, ii) A prescriptive framework that utilizes the learned features for
collaborative sequential design wherein dispersed users effectively dist
ribute their trial & error efforts to improve and fast-track the optimal
design process. I conclude the talk by describing some of our real-world
prototyping and testing efforts.\n \n \nBio: Raed Al Kontar is an as
sistant professor in the Industrial & Operations Engineering department a
t the University of Michigan and an affiliate with the Michigan Institute
for Data Science. Raed's research focuses on collaborative\, distributed
\, and decentralized data science. Raed obtained an undergraduate degree
in civil & environmental engineering and mathematics from the American Un
iversity of Beirut in 2014 and a master's degree in statistics in 2017 an
d a Ph.D. degree in Industrial & System Engineering in 2018\, both from t
he University of Wisconsin-Madison. Raed's research is currently supporte
d by NSF\, including a 2022 CAREER award\, NIH\, NLM\, and various indust
ry collaborators
DURATION:PT1H
DTSTAMP:20231012T163654Z
DTSTART;TZID=America/New_York:20231020T153000
LAST-MODIFIED:20231012T163654Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Collaborative and Federated Data Analytics Beyond Predictive Model
ing
UID:CAL-8a03932d-8a96243a-018a-e6fe16e4-00006c5bdemobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu
:Karen Whitesell
X-BEDEWORK-SPEAKER:Raed Al Kontar\, Assistant Professor\, University of Mi
chigan
X-BEDEWORK-DUKE-SERIES:StatSci Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20230320T161608Z
DESCRIPTION:It is increasingly possible to develop treatments for psychiat
ric disorders by making targeted interventions on the brain. However\, d
esigning an appropriate protocol requires many choices. We propose a meth
od that identifies electrical dynamics across brain regions related to il
lness states or behaviors and employs these patterns to design interventi
on protocols. Specifically\, the observed electrical activity of the bra
in is statistically modeled as a superposition of activity from latent el
ectrical functional connectome (electome) networks. The activity of these
latent networks defines a brain state that predicts disease state\, beha
vior\, or outcomes. These electome networks are explainable in their spec
tral power and directional relationships between brain regions\, facilita
ting the design of testable protocols on key relationships. We present a
case study on social aggression\, where we identify an electome network a
ssociated with aggressive behavior and develop a machine-learning control
led protocol that selectively reduces aggression without affecting pro-so
cial behavior. We conclude with ongoing efforts in causal discovery and
mediation analysis to further understand and improve this system.
DURATION:PT1H
DTSTAMP:20231031T190331Z
DTSTART;TZID=America/New_York:20231103T153000
LAST-MODIFIED:20231031T190331Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Machine Learning to Infer and Control Brain State
UID:CAL-8a018d0d-8b5366bc-018b-871e9aa9-000023e5demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-SPEAKER:David Carlson\, Assistant Professor\, Civil and Environ
mental Engineering\, Duke University
X-BEDEWORK-DUKE-SERIES:StatSci Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20231106T143008Z
DESCRIPTION:In this presentation\, we will introduce data science\, AI\, a
nd biostatistics career opportunities in academic health care. The Duke B
ERD (Biostatistics\, Epidemiology\, and Research Design) Methods Core is
a team of staff and faculty with expertise in data science\, biostatistic
s\, informatics\, and other quantitative areas who collaborate with biome
dical researchers to solve important health-related problems across all a
reas of medicine. Quantitative scientists in the BERD Core design studies
\, implement and design methods and clinical trials\, develop real-time p
rediction models\, and ensure that results are interpreted appropriately
to improve health care. These scientists have exciting careers that enabl
e them to provide high-quality analytics\, facilitate reproducible resear
ch workflows\, and disseminate impactful results in interdisciplinary col
laborative environments. One collaboration we will highlight is with the
Center for AIDS Research (CFAR). We have paid internships to solve proble
ms in the area of in HIV/AIDS research available for Summer 2024!
DURATION:PT1H15M
DTSTAMP:20231107T182137Z
DTSTART;TZID=America/New_York:20231108T150500
LAST-MODIFIED:20231107T182137Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Career Opportunities in Academic Healthcare
UID:CAL-8a018ccf-8b87f80e-018b-a50a768f-000008e9demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=megan.e.kelly@duke.edu:M
egan Kelly Deyncourt
X-BEDEWORK-SPEAKER:Gina-Maria Pomann PhD\, Assistant Professor\, Departmen
t of Biostatistics and Bioinformatics\, Duke University School of Medicin
e and Richard Barfield\, Staff Biostatistician\, Department of Biostatis
tics and Bioinformatics\, Duke University School of Medicine
X-BEDEWORK-DUKE-SERIES:Statistical Science Proseminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
X-BEDEWORK-IMAGE-X1:0
X-BEDEWORK-IMAGE-Y1:0
X-BEDEWORK-IMAGE-X2:1050
X-BEDEWORK-IMAGE-Y2:700
X-BEDEWORK-IMAGE-CROP-WIDTH:1050
X-BEDEWORK-IMAGE-CROP-HEIGHT:700
X-BEDEWORK-IMAGE-ALT-TEXT:portraits of speakers
X-BEDEWORK-IMAGE:/public/Images/Screenshot 2023-11-07 at 1.19.28 PM_202311
07062137PM.png
X-BEDEWORK-THUMB-IMAGE:/public/Images/Screenshot 2023-11-07 at 1.19.28 PM_
20231107062137PM-thumb.png
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20231106T143320Z
DESCRIPTION:Bayesian optimization (BayesOpt) optimizes time-consuming-to-e
valuate objective functions arising in materials design\, drug discovery\
, neural architecture design\, and other applications. It combines a Baye
sian posterior distribution over the objective function with a decision-t
heoretic acquisition function that quantifies the value of objective func
tion and constraint evaluations ("experiments").\n\nWhile BayesOpt is a b
lack-box optimization approach\, we have recently shown that "peeking ins
ide the box" can improve performance by several orders of magnitude. Key
to this approach are statistical methods that incorporate additional inf
ormation beyond the values of the objective function. For example\, when
optimizing quality in a manufacturing process\, these methods incorporate
observations of quality after each stage of the process\, not just the q
uality of the final output.\n\nThis idea also offer a new way to interact
with humans who have trouble choosing a single objective function. Rathe
r than estimating a Pareto frontier like traditional multi-objective opti
mization methods\, we can model the human as having a utility function dr
awn from a Bayesian prior. By iteratively updating a posterior on the hu
man's utility function in response to questions ("which tradeoff between
cost and quality do you like better?") and using this knowledge to priori
tize experiments\, we can identify a set of solutions whose maximum utili
ty is likely to be large. This approach better leverages information abou
t user preferences to provide much better efficiency than traditional mul
t-objective methods.\n\nWe describe the ideas behind these approaches and
how they are being used to design novel energy materials in collaboratio
n and optimize online platforms.
DURATION:PT1H
DTSTAMP:20231106T143756Z
DTSTART;TZID=America/New_York:20231110T153000
LAST-MODIFIED:20231106T143756Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Grey-Box Bayesian Optimization for Human-in-the-loop Optimization
UID:CAL-8a018ccf-8b87f80e-018b-a50d657b-000008eademobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-SPEAKER:Peter Frazier Eleanor and Howard Morgan Professor Opera
tions Research and Information Engineering Cornell University
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-DUKE-SERIES:Statistical Science Seminar
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=Karen.Whitesell@duke.edu
:Karen Whitesell
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Africa focus
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=2c918084-613c4fe3-0161-43be89e7-00003081:Thakkar\,
Rohini
CREATED:20231031T190328Z
DESCRIPTION:Rapid-fire military takeovers in Mali\, Burkina Faso\, and Nig
er\; Wagner - the Kremlin's proxy force - moving into the region while th
e French army is moving out amidst a groundswell of hostility against Fra
nce's postcolonial presence\; and the fastest-growing Jihadist insurgency
in the world... Of late\, the swath of arid land stretching across Afric
a south of the Sahara has been much in the news. Five experts will engage
in a timely conversation about the Sahel. \n\nLeif Brottem is Associate
Professor of Global Development Studies at Grinnell College in the state
of Iowa. \n\nMarc-Antoine Pérouse de Montclos\, a Doctor in political sc
ience\, is a Senior Researcher at the Institut de recherche pour le dével
oppement (IRD). \n\nAlioune Sow is a joint appointment in French and Afri
can Studies at the University of Florida. \n\nStephen W. Smith\, Ph.D.\,
teaches African Studies at Duke with a research focus on conflict analysi
s\, demography/population age structure and Franco-African postcolonialit
y..
DURATION:PT1H30M
DTSTAMP:20231101T164812Z
DTSTART;TZID=America/New_York:20231116T113000
LAST-MODIFIED:20231101T164812Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:The Sahel Region: Coups\, Jihadism\, Wagner & Anti-French Sentimen
ts
UID:CAL-8a018d0d-8b5366bc-018b-871e8cda-000023e4demobedework@mysite.edu
URL:https://global.duke.edu/dai-events
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Africa focus:/user/public-us
er/Topic of Event Focused on a Country or Continent (if applicable)/Afric
a focus
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-SPEAKER:Leif Brottem (Grinnell College)\, Marc-Antoine Pérouse
de Montclos (IRD\, Paris)\, Alioune Sow (University of Florida)\, Stephen
Smith (Duke)
X-BEDEWORK-IMAGE-X1:0
X-BEDEWORK-IMAGE-Y1:0
X-BEDEWORK-IMAGE-X2:661.5
X-BEDEWORK-IMAGE-Y2:441
X-BEDEWORK-IMAGE-CROP-WIDTH:661.5
X-BEDEWORK-IMAGE-CROP-HEIGHT:441
X-BEDEWORK-IMAGE-ALT-TEXT:Flyer of the event which has title\, day\, date\
, time of the event. Speakers&\;amp\;amp\;#39\; headshots\, names and
affiliations\, Africa Initiative logo and image of a few soldiers.
X-BEDEWORK-SUBMITTEDBY:rt54 for Africa Initiative (agrp_AfricaInitiative)
X-BEDEWORK-IMAGE:/public/Images/v2 The Sahel Region Flyer_20231101044220PM
.png
X-BEDEWORK-THUMB-IMAGE:/public/Images/v2 The Sahel Region Flyer_2023110104
4220PM-thumb.png
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20230320T161608Z
DESCRIPTION:Transportation of measure underlies many powerful tools for Ba
yesian inference\, density estimation\, and generative modeling. The cent
ral idea is to deterministically couple a probability measure of interest
with a tractable "reference" measure (e.g.\, a standard Gaussian). Such
couplings are induced by transport maps and enable direct simulation from
the desired measure simply by evaluating the transport map at samples fr
om the reference. \n\nWhile an enormous variety of representations and co
nstructive algorithms for transport maps have been proposed in recent yea
rs\, it is inevitably advantageous to exploit the potential for low-dimen
sional structure in the associated probability measures. I will discuss t
wo such notions of low-dimensional structure\, and their interplay with t
ransport-driven methods for sampling and inference. The first seeks to ap
proximate a high-dimensional target measure as a low-dimensional update o
f a dominating reference measure. The second is low-rank conditional stru
cture\, where the goal is to replace conditioning variables with low-dime
nsional projections or summaries. In both cases\, under appropriate assum
ptions on the reference or target measures\, one can derive gradient-base
d upper bounds on the associated approximation error and minimize these b
ounds to identify good subspaces for approximation. The associated subspa
ces then dictate specific structural ansatzes for transport maps that rep
resent the target of interest.\n\nI will showcase several algorithmic ins
tantiations of this idea\, with examples drawn from Bayesian inverse prob
lems\, data assimilation\, and/or simulation-based inference.
DURATION:PT1H
DTSTAMP:20231113T174258Z
DTSTART;TZID=America/New_York:20231117T153000
LAST-MODIFIED:20231113T174258Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:On low-dimensional structure in transport and inference
UID:CAL-8a018ccf-8b87f80e-018b-c9c787f2-00005366demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-SPEAKER:Youssef Marzouk\, Professor\, MIT
X-BEDEWORK-DUKE-SERIES:StatSci Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20230320T161608Z
DESCRIPTION:We study the problem of distribution-free dependence detection
and modeling through the new framework of binary expansion statistics (B
EStat). The binary expansion testing (BET) avoids the problem of non-unif
orm consistency and improves upon a wide class of commonly used methods (
a) by achieving the minimax rate in sample size requirement for reliable
power and (b) by providing clear interpretations of global relationships
upon rejection of independence. The binary expansion approach also connec
ts the symmetry statistics with the current computing system to facilitat
e efficient bitwise implementation. Modeling with the binary expansion li
near effect (BELIEF) is motivated by the fact that two linearly uncorrela
ted binary variables must be also independent. Inferences from BELIEF are
easily interpretable because they describe the association of binary var
iables in the language of linear models\, yielding convenient theoretical
insight and striking parallels with the Gaussian world. With BELIEF\, on
e may study generalized linear models (GLM) through transparent linear mo
dels\, providing insight into how modeling is affected by the choice of l
ink. We explore these phenomena and provide a host of related theoretical
results. This is joint work with Benjamin Brown and Xiao-Li Meng.
DURATION:PT1H
DTSTAMP:20231115T185834Z
DTSTART;TZID=America/New_York:20231201T153000
LAST-MODIFIED:20231115T185834Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:BET and BELIEF
UID:CAL-8a018ccf-8b87f80e-018b-d4597622-00003ff8demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-SPEAKER:Kai Zhang\, Associate Professor\, UNC
X-BEDEWORK-DUKE-SERIES:StatSci Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20230320T161608Z
DESCRIPTION:In problems such as variable selection and graph estimation\,
models are characterized by Boolean logical structure such as presence or
absence of a variable or an edge. Consequently\, false positive and fals
e negative errors can be specified as the number of variables or edges th
at are incorrectly included/excluded in an estimated model. However\, the
re are several other problems such as ranking\, clustering\, and causal i
nference in which the associated model classes do not admit transparent n
otions of false positive and false negative errors due to the lack of an
underlying Boolean logical structure. In this paper\, we present a generi
c approach to endow a collection of models with partial order structure\,
which leads to a hierarchical organization of model classes as well as n
atural analogs of false positive and false negative errors. We describe m
odel selection procedures that provide false positive error control in ou
r general setting and we illustrate their utility with numerical experime
nts.\nThis is joint work with Peter Buehlmann and Venkat Chandrasekaran.
DURATION:PT1H
DTSTAMP:20231201T182334Z
DTSTART;TZID=America/New_York:20231208T153000
LAST-MODIFIED:20231201T182334Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Model selection over partially ordered sets
UID:CAL-8a018ccf-8b87f80e-018c-269f2d1b-0000757cdemobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-SPEAKER:Armeen Taeb\, Assistant Professor\, University of Washi
ngton
X-BEDEWORK-DUKE-SERIES:StatSci Seminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu
:Karen Whitesell
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20231229T143339Z
DESCRIPTION:The total electron content (TEC) maps can be used to estimate
the signal delay of GPS due to the ionospheric electron content between a
receiver and a satellite. This delay can result in a GPS positioning err
or. Thus\, it is important to monitor and forecast the TEC maps. However\
, the observed TEC maps have big patches of missingness in the ocean and
scattered small areas on the land. Thus\, precise imputation and predicti
on of the TEC maps are crucial in space weather forecasting. \n\nIn this
talk\, I first present several extensions of existing matrix completion a
lgorithms to achieve TEC map reconstruction\, accounting for spatial smoo
thness and temporal consistency while preserving essential structures of
the TEC maps. We show that our proposed method achieves better reconstruc
ted TEC maps as compared to existing methods in the literature. I will al
so briefly describe the use of our large-scale complete TEC database. The
n\, I present a new model for forecasting time series data distributed on
a matrix-shaped spatial grid\, using the historical spatiotemporal data
and auxiliary vector-valued time series data. Large sample asymptotics of
the estimators for both finite and high dimensional settings are establi
shed. Performances of the model are validated with extensive simulation s
tudies and an application to forecast the global TEC distributions.
DURATION:PT1H
DTSTAMP:20240102T213722Z
DTSTART;TZID=America/New_York:20240112T153000
LAST-MODIFIED:20240102T213722Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Video Imputation and Prediction Methods with Applications in Space
Weather
UID:CAL-8a018ccf-8b87f80e-018c-b5febd0f-00003128demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=Karen.whitesell@duke.edu
:Karen Whitesell
X-BEDEWORK-SPEAKER:Yang Chen\, Assistant Professor\, University of Michiga
n
X-BEDEWORK-DUKE-SERIES:Statistical Science Seminar Series
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20240113T005531Z
DESCRIPTION:The biochemical functions of proteins\, such as catalyzing a c
hemical reaction or binding to a virus\, are typically conferred by the g
eometry of only a handful of atoms. This arrangement of atoms\, known as
a motif\, is structurally supported by the rest of the protein\, referre
d to as a scaffold. A central task in protein design is to identify a di
verse set of stabilizing scaffolds to support a motif known or theorized
to confer function. This long-standing challenge is known as the motif-sc
affolding problem.\n \nIn this talk\, I describe a statistical approach I
have developed to address the motif-scaffolding problem. My approach in
volves (1) estimating a distribution supported on realizable protein stru
ctures and (2) sampling scaffolds from this distribution conditioned on a
motif. For step (1) I adapt diffusion generative models to fit example
protein structures from nature. For step (2) I develop sequential monte
carlo algorithms to sample from the conditional distributions of these mo
dels. I finally describe how\, with experimental and computational colla
borators\, I have generalized and scaled this approach to generate and ex
perimentally validate hundreds of proteins with various functional specif
ications.\n \nBio:\nBrian Trippe is a postdoctoral fellow at Columbia Uni
versity in the Department of Statistics\, and a visiting researcher at th
e Institute for Protein Design at the University of Washington. He comple
ted his Ph.D. in Computational and Systems Biology at the Massachusetts I
nstitute of Technology where worked on Bayesian methods for inference in
hierarchical linear models. In his research\, Brian develops statistical
machine learning methods to address challenges in biotechnology and medic
ine\, with a focus on generative modeling and inference algorithms for pr
otein engineering.
DURATION:PT1H
DTSTAMP:20240116T171502Z
DTSTART;TZID=America/New_York:20240119T153000
LAST-MODIFIED:20240116T171502Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Probabilistic methods for designing functional protein structures
UID:CAL-8a008bcc-8cfbd545-018d-00511b7e-00005306demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu
:Karen Whitesell
X-BEDEWORK-SPEAKER:Brian Trippe\, Columbia University
X-BEDEWORK-DUKE-SERIES:Statistical Science
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20240109T033728Z
DESCRIPTION:Optimization techniques\, such as dual ascent\, alternating di
rection method of multipliers\, and majorization-minimization\, are widel
y used in high-dimensional applications. The strengths of optimization ar
e the high computing efficiency and the ease of inducing point estimates
on useful constrained spaces\, such as those satisfying low rank\, low ca
rdinality or combinatorial structure. For uncertainty quantification arou
nd point estimate\, a popular generalized Bayes solution known as Gibbs p
osterior exponentiates the negative loss function\, and forms a posterior
density. Despite successful theoretic justifications\, Gibbs posterior d
istribution is supported in a high-dimensional space and\, hence often do
es not inherit nice properties in computing efficiency and constraints fr
om optimization. In this work\, we are motivated by a discovery that a la
rge class of penalized profile likelihoods\, which partially maximize ove
r a subset of parameters\, in fact enjoy equivalence to another generativ
e model for the data. This leads us to explore a new generalized Bayes ap
proach that views the likelihood as an equality-constrained function\, ba
sed on data\, parameters\, and a conditionally deterministic latent varia
ble equal to an optimization solution. This new likelihood can be justifi
ed as a special case of augmented likelihood where the latent variable is
typically exploited to model dependency among the data. Therefore\, this
framework coined "bridged posterior'' conforms to the Bayesian methodolo
gy. A surprising theoretical finding is that under mild conditions\, the
square root n-adjusted bridged posterior distribution of the parameters c
onverges to the same asymptotical normal that the canonical integrated po
sterior converges to. Therefore\, our results formally dispel a long-time
belief that partial optimization over latent variables might lead to an
underestimation of parameter uncertainty. We demonstrate the practical ad
vantages of our approach in applications\, such as classification with pa
rtially labeled data and harmonization of multiple brain scan networks.
DURATION:PT1H
DTSTAMP:20240112T200140Z
DTSTART;TZID=America/New_York:20240122T114500
LAST-MODIFIED:20240112T200140Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Bridged Posterior: Optimization\, Profile Likelihood and a New App
roach of Generalized Bayes
UID:CAL-8a018ccf-8b87f80e-018c-ec4beffe-000055e3demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu
:Karen Whitesell
X-BEDEWORK-SPEAKER:Leo Duan\, Assistant Professor\, University of Florida
X-BEDEWORK-DUKE-SERIES:Statistical Science
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20240110T024331Z
DESCRIPTION:The exploratory and interactive nature of modern data analysis
often introduces selection bias\, posing challenges for traditional stat
istical inference methods. A common strategy to address this bias is by c
onditioning on the selection event. However\, this often results in a con
ditional distribution that is intractable and requires Markov chain Monte
Carlo (MCMC) sampling for inference. Notably\, some of the most widely u
sed selection algorithms yield selection events that can be characterized
as polyhedra\, such as the lasso for variable selection and the epsilon-
greedy algorithm for multi-armed bandit problems. This talk will present
a method that is tailored for conducting inference following polyhedral s
election. The method transforms the variables constrained within a polyhe
dron into variables within a unit cube\, allowing for exact sampling. Com
pared to MCMC\, the proposed method offers superior speed and accuracy\,
providing a practical and efficient approach for conditional selective in
ference. Additionally\, it facilitates the computation of the selection-a
djusted maximum likelihood estimator\, enabling MLE-based inference. Nume
rical results demonstrate the enhanced performance of the proposed method
compared to alternative approaches for selective inference.
DURATION:PT1H
DTSTAMP:20240110T024331Z
DTSTART;TZID=America/New_York:20240126T153000
LAST-MODIFIED:20240110T024331Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:An Exact Sampler for Inference after Polyhedral Selection
UID:CAL-8a018ccf-8b87f80e-018c-f140e857-0000167edemobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-SPEAKER:Sifan Liu\, Stanford University
X-BEDEWORK-DUKE-SERIES:Statistical Science
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu
:Karen Whitesell
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20240110T140507Z
DESCRIPTION:Mixed effect modeling for longitudinal data is challenging whe
n the observed data are random objects\, which are complex data taking va
lues in a general metric space without either global linear or local line
ar (Riemannian) structure. In such settings the classical additive error
model and distributional assumptions are unattainable. Due to the rapid a
dvancement of technology\, longitudinal data containing complex random ob
jects\, such as covariance matrices\, data on Riemannian manifolds\, and
probability distributions are becoming more common. Addressing this chall
enge\, we develop a mixed-effects regression for data in geodesic spaces\
, where the underlying mean response trajectories are geodesics in the me
tric space and the deviations of the observations from the model are quan
tified by perturbation maps or transports. A key finding is that the geod
esic trajectories assumption for the case of random objects is a natural
extension of the linearity assumption in the standard Euclidean scenario
to the case of general geodesic metric spaces. Geodesics can be recovered
from noisy observations by exploiting a connection between the geodesic
path and the path obtained by global Fréchet regression for random object
s. The effect of baseline Euclidean covariates on the geodesic paths is m
odeled by another Fréchet regression step. We study the asymptotic conver
gence of the proposed estimates and provide illustrations through simulat
ions and real-data applications.
DURATION:PT1H
DTSTAMP:20240127T192305Z
DTSTART;TZID=America/New_York:20240129T114500
LAST-MODIFIED:20240127T192305Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:CANCELLED:Geodesic Mixed Effects Models for Repeatedly Observed/Lo
ngitudinal Random Objects
UID:CAL-8a018ccf-8b87f80e-018c-f3b0eca1-00002992demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu
:Karen Whitesell
X-BEDEWORK-SPEAKER:Satarupa Bhattacharjee\, Pennsylvania State
X-BEDEWORK-DUKE-SERIES:Statistical Science
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20240110T022314Z
DESCRIPTION:Monte Carlo methods span a range of disciplines\, drawing inte
rest from statisticians\, computer scientists\, physicists\, among others
. In the Monte Carlo workflow\, the upstream task involves designing and
analyzing sampling algorithms\, while the downstream task focuses on effi
ciently using these samples to form estimators. In this talk\, I will dis
cuss recent developments of both aspects. The first part introduces the '
Spectral Telescope' framework for analyzing Gibbs samplers\, and discusse
s its relationship with the spectral independence technique recently deve
loped in theoretical computer science. The second part focuses on the dev
elopment of unbiased estimators through the combination of Markov Chain M
onte Carlo and Multilevel Monte Carlo methods\, highlighting their potent
ial in parallel computing.\n\nBiosketch: Guanyang Wang is an Assistant Pr
ofessor in the Department of Statistics at Rutgers University. He complet
ed his Ph.D. in Mathematics with a Ph.D. minor in Statistics at Stanford
University\, advised by Professor Persi Diaconis. Guanyang Wang's researc
h primarily centers on Monte Carlo methods\, applied probability\, and st
atistical computing. Recently\, he is also working on quantum computing.
His research receives support from both the NSF and an Adobe Data Science
Research Award.
DURATION:PT1H
DTSTAMP:20240110T165332Z
DTSTART;TZID=America/New_York:20240202T153000
LAST-MODIFIED:20240110T165332Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:The Many Facets of Monte Carlo Methods: From Sampling Algorithms t
o Unbiased Estimators
UID:CAL-8a018ccf-8b87f80e-018c-f12e53cb-000015bddemobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu
:Karen Whitesell
X-BEDEWORK-SPEAKER:Guanyang Wang\, Assistant Professor\, Rutgers Universit
y
X-BEDEWORK-DUKE-SERIES:Statistical Science
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20240110T184203Z
DESCRIPTION:Recent interest has centered on uncertainty quantification for
machine learning models. For the most part\, this work has assumed indep
endence of the observations. However\, many of the most important problem
s arising across scientific fields\, from genomics to climate science\, i
nvolve systems where dependence cannot be ignored. In this talk\, I will
investigate inference on machine learning models in the presence of depen
dence. \n\nIn the first part of my talk\, I will consider a common practi
ce in the field of genomics in which researchers compute a correlation ma
trix between genes and threshold its elements in order to extract groups
of independent genes. I will describe how to construct valid p-values ass
ociated with these discovered groups that properly account for the group
selection process. While this is related to the literature on selective
inference developed in the past decade\, this work involves inference abo
ut the covariance matrix rather than the mean\, and therefore requires an
entirely new technical toolset. This same toolset can be applied to quan
tify the uncertainty associated with canonical correlation analysis after
feature screening. \n\nIn the second part of my talk\, I will turn to an
important problem in the field of oceanography as it relates to climate
science. Oceanographers have recently applied random forests to estimate
carbon export production\, a key quantity of interest\, at a given locati
on in the ocean\; they then wish to sum the estimates across the world's
oceans to obtain an estimate of global export production. While quantifyi
ng uncertainty associated with a single estimate is relatively straightfo
rward\, quantifying uncertainty of the summed estimates is not\, due to t
heir complex dependence structure. I will adapt the theory of V-statistic
s to this dependent data setting in order to establish a central limit th
eorem for the summed estimates\, which can be used to quantify the uncert
ainty associated with global export production across the world's oceans.
\n\nThis is joint work with my postdoctoral supervisors\, Daniela Witten
(University of Washington) and Jacob Bien (University of Southern Califor
nia).
DURATION:PT1H
DTSTAMP:20240126T191301Z
DTSTART;TZID=America/New_York:20240205T114500
LAST-MODIFIED:20240126T191301Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Inference for machine learning under dependence
UID:CAL-8a018ccf-8b87f80e-018c-f4ae7643-00007e00demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu
:Karen Whitesell
X-BEDEWORK-SPEAKER:Arkajyoti Saha\, University of Washington
X-BEDEWORK-DUKE-SERIES:Statistical Science
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20240122T180117Z
DESCRIPTION:This presentation offers insights into the work environment at
JMP Statistical Discovery LLC\, a prominent statistical software company
\, with a specific focus on the Research and Development (R&D) division.
Within this dynamic industry\, I'll showcase unique aspects of working at
JMP\, emphasizing the collaborative and innovative culture that defines
the company. In this talk\, I will share insights on software testing\, a
critical aspect of the company's operations. While the testing group at
JMP have graduate degrees in statistics and related fields\, software tes
ting is not a topic covered in most statistics programs. This talk will d
iscuss the challenges and intricacies of software testing and cutting-edg
e testing techniques used within JMP.\n \nDr. Ryan Lekivetz is a Senior M
anager of Advanced Analytics R&D at JMP\, heading the Design of Experimen
ts (DOE) and Reliability Development team. Ryan earned his doctorate in s
tatistics from Simon Fraser University in Burnaby\, British Columbia. He
has published papers on DOE topics in peer-reviewed journals and holds ma
ny patents that he shares with his team members. His research interests i
nclude design of experiments\, combinatorial testing\, and assessing the
usability of statistical software through designed experiments.
DURATION:PT1H15M
DTSTAMP:20240126T210345Z
DTSTART;TZID=America/New_York:20240207T150500
LAST-MODIFIED:20240126T210345Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:A Day in the Life: JMP R&D
UID:CAL-8a0292fd-8d13410f-018d-32557553-00001ea8demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=megan.deyncourt@duke.edu
:Megan Deyncourt
X-BEDEWORK-SPEAKER:Dr. Ryan Lekivetz\, Senior Manager\, Advanced Analytics
R&D\, JMP
X-BEDEWORK-DUKE-SERIES:Statistical Science Proseminar
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20240112T194732Z
DESCRIPTION:In this talk\, I will discuss semi-parametric estimation when
nuisance parameters cannot be estimated consistently\, focusing in partic
ular on the estimation of average treatment effects\, conditional correla
tions\, and linear effects under high-dimensional GLM specifications. In
this challenging regime\, even standard doubly-robust estimators can be i
nconsistent. I describe novel approaches which enjoy consistency guarante
es for low-dimensional target parameters even though standard approaches
fail. For some target parameters\, these guarantees can also be used for
inference. Finally\, I will provide my perspective on the broader implica
tions of this work for designing methods which are less sensitive to bias
es from high-dimensional prediction models.
DURATION:PT1H
DTSTAMP:20240117T144245Z
DTSTART;TZID=America/New_York:20240209T153000
LAST-MODIFIED:20240117T144245Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Debiasing in the inconsistency regime
UID:CAL-8a008bcc-8cfbd545-018c-ff372232-00003c95demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu
:Karen Whitesell
X-BEDEWORK-SPEAKER:Michael Celentano\, University of California\, Berkeley
X-BEDEWORK-DUKE-SERIES:Statistical Science
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20240216T212156Z
DESCRIPTION:Ensemble decision tree methods such as XGBoost\, RF\, and BART
have gained enormous popularity in data science for their superior perfo
rmance in machine learning regression and classification tasks. In this p
aper\, we develop a new Bayesian graph-split-based additive decision tree
s method\, called GS-BART\, to improve the performance of Bayesian additi
ve decision trees for complex dependent data with graph relations. The ne
w method adopts a highly flexible split rule complying with graph structu
re to relax the axis-parallel split rule assumption in most existing ense
mble decision tree models. We design a scalable informed MCMC algorithm l
everaging a gradient-based recursive algorithm on spanning trees or chain
s to sample the graph-split-based decision tree. The superior performance
of the method over conventional ensemble tree models and gaussian proces
s models is illustrated in various regression and classification tasks fo
r spatial and network data analysis.
DURATION:PT1H
DTSTAMP:20240216T220058Z
DTSTART;TZID=America/New_York:20240223T153000
LAST-MODIFIED:20240216T220058Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:GS-BART: Graph split additive decision trees for classification an
d nonparametric regression of spatial and network data
UID:CAL-8a0292fd-8d13410f-018d-b3cc235e-000015c7demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu
:Karen Whitesell
X-BEDEWORK-SPEAKER:Huiyan Sang Professor of Statistics Texas A&M Universit
y
X-BEDEWORK-DUKE-SERIES:Statistical Science
X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS
cience)
END:VEVENT
END:VCALENDAR