Accurate multimodal prediction-spanning tabular, textual, and visual data-is crucial for advancing analytics across diverse domains. However, traditional models often struggle to integrate… read more about Generative Distribution Prediction for Multimodal Learning »
Abstract: Instrumental variables are a popular tool to infer causal effects under unobserved confounding, but choosing suitable instruments is challenging in practice. We propose gIVBMA, a Bayesian… read more about Bayesian Model Averaging in Causal Instrumental Variable Models »
Min-norm interpolators naturally emerge as implicit regularized limits of modern machine learning algorithms. Recently, their out-of-distribution risk was studied when test samples are unavailable… read more about Quantifying the Effects of Transfer Learning in Min-norm Interpolation »
Random partitions are fundamental probabilistic objects in Bayesian statistics, particularly in nonparametric models, as they enable flexible clustering and relax strong distributional assumptions… read more about Random Partitions for Multi-view Data: How to Encode Repeated Measures Design into Nonparametric Bayesian Models. »
ASA DataFest @ Duke 2025 will take place in Penn Pavilion April 4-6 -- register here!ASA DataFest is an international data analysis competition where teams of undergraduate students attack a large,… read more about ASA DataFest@Duke! »
The Statistical Science Department invites all to attend the defense of this dissertation. read more about Bayesian Deep Discrete Latent Structures »
The Statistical Science Department invites all to attend the defense of this dissertation. read more about Bayesian Strategies for Differential Privacy and Risk Assessment »
The Statistical Science Department invites all to attend the defense of this dissertation. read more about Causal Inference Theory and Methods for Design and Analysis of Observational Studies »
The Statistical Science Department invites all to attend the defense of this dissertation. read more about Uniform Large Sample Theory of Generalized Frechet Means »
The Statistical Science Department invites all to attend the defense of this dissertation. read more about Adaptive Experimentation and Decision-Making: From Bayesian Optimization to Multi-armed Bandits »
The Statistical Science Department invites all to attend the defense of this dissertation. read more about Bayesian Inference for Discrete Structures »
The denoising diffusion probabilistic model (DDPM) has become a cornerstone of generative AI. While sharp convergence guarantees have been established for DDPM, the iteration complexity typically… read more about To Intrinsic Dimension and Beyond: Efficient Sampling in Diffusion Models »
A major challenge in the age of Big Data is the integration of disparate data types into a single data analysis. That is tackled here in the context of data blocks measured on a common set of… read more about Data Integration Via Analysis of Subspaces (DIVAS) »
Verbal autopsy (VA) algorithms are routinely employed in low-and middle-income countries to determine individual causes of death (COD), which are then aggregated to estimate population-level cause-… read more about Bayesian Modeling of Misclassification Matrices for Improved Verbal Autopsy-Based Mortality Estimates in LMICs »
Sampling from a target distribution is a recurring theme in statistics and generative artificial intelligence (AI). In Bayesian statistics, posterior sampling offers a flexible inferential framework… read more about Modern Sampling Paradigms: from Posterior Sampling to Generative AI »
Feature/variable selection is a fundamental technique in data science that aims to identify the relevant features in a dataset. A critical component of feature selection is false positive control.… read more about Integrated Path Stability Selection »
The success of Bayesian inference with MCMC depends critically on Markov chains rapidly reaching the posterior distribution. Despite the plentitude of inferential theory for posteriors in Bayesian… read more about On Mixing Rates for Bayesian CART »
Dietary patterns are essential for understanding dietary behaviors and their health implications in nutritional epidemiology, yet complexities in dietary assessments pose analytical challenges.… read more about Latent Variable Models for Advancing Nutrition Epidemiology »
SWS Distinguished Lecture and Lunch SeriesThe Society for Women in Science (SWS) is a community created by women scientists for women scientists. We welcome scientists from all STEM disciplines,… read more about Overview of Society for Women in Science »
We derive an asymptotic expansion of posterior integrals in the regime in which dimension grows together with sample size. We also present related work on the accuracy of the Laplace approximation (… read more about Asymptotics of High-Dimensional Bayesian Inference »
We investigate inference for a single coordinate in high-dimensional linear regression, focusing on settings where there is a single parameter of interest (such as a treatment effect) in the presence… read more about Bayesian Linear Regression with a Sparse Prior: Inference for a Single Coordinate »
This talk introduces a novel framework for multitask learning in high-dimensional time series through hypothesis testing and data integration. The proposed procedure tests for shared structures… read more about Multitask Learning for High-dimensional Time Series »
Speakers: Sam Rosen and Riccardo Rossetti Moderator: Shuo WangSpeaker: Sam RosenTitle: New Finite-Sample Bounds in the Convex Clustering RegimeAbstract: Convex Clustering is… read more about STA 701 Talk »
For several decades, political scientists have collected data sets of "dyadic events"-i.e., micro-records of the form "country i took action a to county j at time t". Such data sets provide an… read more about Probabilistic Tensor Decomposition Model for Measuring Complex Dependence Structure in Sparse Dyadic Event Data »
The Statistical Science Department encourages all to attend the defense of this dissertation. read more about Nonparametric Mixture Models for Covariance Matrix Estimation and Hypothesis Testing with Applications in Neuroscience »