New Advances in Statistics and Data Science
May 24-26, 2022, Honolulu, Hawaii
Day 3 (May 26, 2022): New Advances in Machine Learning
8:40am - 10:20am: Session 1 (Chair: Tracy Ke)
- Yingying Fan: Asymptotic Properties of High-dimensional Random Forests
(abstract)
- Edgar Dobriban: T-Cal: An Optimal Test for the Calibration of Predictive Models
(abstract)
- Jun S. Liu: Statistics Meet Neural Networks: Bootstrap, Cross-Validations, and Beyond
(abstract)
- Jianqing Fan: How Do Noise Tails Impact on Deep ReLU Networks?
(abstract)
10:20am - 10:40am: Coffee break
11:20am - 12:10pm: Session 2 (Chair: Jason Lee)
- Tony Cai: Transfer Learning: Optimality and Adaptive Algorithms
(abstract)
- Patrick Rubin-Delanchy: Manifold Structure in Graph Embeddings
(abstract)
- Tengyu Ma: Understanding Self-supervised Learning
(abstract)
12:10pm - 1:40pm: Lunch break
1:40pm - 3:20pm: Session 3 (Chair: Qi Lei)
- Jason Lee: Offline Reinforcement Learning with only Realizability
(abstract)
- Yuejie Chi: Offline Reinforcement Learning: Towards Optimal Sample Complexities
(abstract)
- Adel Javanmard: The Curse of Overparametrization in Adversarial Training
(abstract)
- Simon Du: When is Offline Two-Player Zero-Sum Markov Game Solvable?
(abstract)
3:20pm - 3:40pm: Break
3:40pm - 5:20pm: Session 4 (Chair: Yuejie Chi)
- Quan Zhou: Informed MCMC Sampling for High-dimensional Model Selection Problems
(abstract)
- Krishna Balasubramanian: Towards a Theory of Non-Log-Concave Sampling
(abstract)
- Boxiang Wang: Sparse Convoluted Rank Regression in High Dimensions
(abstract)
- Yuqi Gu: Blessing of Latent Dependence and Identifiable Deep Modeling of Discrete Latent Variables
(abstract)