09:00-09:30 Breakfast at lecture hall
09:30-09:40 Jay Sethuraman: Opening
09:40-10:10 Ming Yuan: Statistical Optimality and Computational Tractability of ICA
10:15-10:35 Zhenyuan Liu: Bootstrap in High Dimension with Low Computation
10:40-11:10 Cedric Josz: Global convergence of the gradient method for functions definable in o-minimal structures
Coffee Break
11:30-12:00 Paul Grigas: Risk Bounds, Consistency, and Online Decision-Making with a Smart Predict-then-Optimize Method
12:05-12:25 Lekshmi Ramesh: Multiple Support Recovery Using Very Few Measurements Per Sample
12:30-01:00 Anish Agarwal: Synthetic Blip Effects: A Causal Framework for Linear Dynamical Systems
Lunch catered light lunch at Mudd 301
02:40-03:10 Rachel Cummings: Thompson Sampling Itself is Differentially Private
03:15-03:35 Ye Tian: Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness
03:40-04:10 Bianca Dumitrascu: Explainable tools for Single-Cell Regulatory Genomics
Coffee Break
04:30-05:00 Nikolaos Ignatiadis: Empirical partially Bayes multiple testing and compound χ² decisions
05:05-05:25 Chenyang Zhong: Mallows Permutation Models with L1 and L2 Distances
05:30-06:00 Thibaut Mastrolia: Incentive to shape equilibria in double auction markets
09:00-09:30 Breakfast at lecture hall
09:30-10:00 David Yao: Polynomial Voting Rules
10:05-10:25 Luhuan Wu: Scaling up variational gaussian processes by nearest neighbor approximation
10:30-11:00 Anil Aswani: Repeated Principal-Agent Games with Unobserved Agent Rewards
Coffee Break
11:20-11:50 Yuqi Gu: Identifiable Deep Generative Models with Discrete Latent Layers
11:55-12:15 Hanyang Zhao: Policy optimization in continuous time and space
12:20-12:50 Kaizheng Wang: Pseudo-Labeling for Kernel Ridge Regression under Covariate Shift
Lunch catered light lunch at Mudd 301
02:20-02:50 Song Mei: Advances in Partially Observable Reinforcement Learning: Structural conditions and Efficient algorithms
02:55-03:15 Haotian Gu: Feasibility and Transferability of Transfer Learning
03:20-03:50 Yongchan Kwon: Data Valuation: Shapley Value and Beyond
Coffee Break
04:10-04:40 Rajan Udwani: Submodular Order Functions and Assortment Optimization
04:45-05:05 Xinyu Li: Adversarial Training for Gradient Descent: Analysis through its Continuous-time Approximation
05:10-05:40 Bodhisattva Sen: Multivariate Distribution-free testing using Optimal Transport
Anish Agarwal, Columbia IEOR
Anil Aswani, Berkeley IEOR
Rachel Cummings, Columbia IEOR
Bianca Dumitrascu, Columbia Stat
Paul Grigas, Berkeley IEOR
Haotian Gu, , Berkeley Math
Yuqi Gu, Columbia Stat
Nikolaos Ignatiadis, Columbia Stat
Cedric Josz, Columbia IEOR
Yongchan Kwon, Columbia Stat
Xinyu Li, Berkeley IEOR
Zhenyuan Liu, Columbia IEOR
Thibaut Mastrolia, Berkeley IEOR
Song Mei, Berkeley Stat and EECS
Lekshmi Ramesh, Columbia Stat
Bodhisattva Sen, Columbia Stat
Ye Tian, Columbia Stat
Rajan Udwani, Berkeley IEOR
Kaizheng Wang, Columbia IEOR
Luhuan Wu, Columbia Stat
David Yao, Columbia IEOR
Ming Yuan, Columbia Stat
Hanyang Zhao, Columbia IEOR
Chenyang Zhong, Columbia Stat
Xin Guo, Berkeley IEOR
Marcel Nutz, Columbia Stat and Math
Wenpin Tang, Columbia IEOR
Chenyang Zhong, Columbia Stat
We thank the generous supports from Columbia Statistics, Columbia IEOR and DSI/FBA Ctr.