1 citations · 3 across the 6 of their papers we have counts for
6 papers
Principled Out-of-Distribution Generalization via Simplicity
Jiawei Ge, Amanda Wang, Shange Tang +1
Modern foundation models exhibit remarkable out-of-distribution (OOD) generalization, solving tasks far beyond the support of their training data. However, the theoretical principl…
Is Elo Rating Reliable? A Study Under Model Misspecification
Shange Tang, Yuanhao Wang, Chi Jin
Elo rating, widely used for skill assessment across diverse domains ranging from competitive games to large language models, is often understood as an incremental update algorithm…
MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations
Kaixuan Huang, Jiacheng Guo, Zihao Li +15
Large language models have demonstrated impressive performance on challenging mathematical reasoning tasks, which has triggered the discussion of whether the performance is achieve…
Benign Overfitting in Out-of-Distribution Generalization of Linear Models
Shange Tang, Jiayun Wu, Jianqing Fan +1
Benign overfitting refers to the phenomenon where an over-parameterized model fits the training data perfectly, including noise in the data, but still generalizes well to the unsee…
Securing Equal Share: A Principled Approach for Learning Multiplayer Symmetric Games
Jiawei Ge, Yuanhao Wang, Wenzhe Li +1
This paper examines multiplayer symmetric constant-sum games with more than two players in a competitive setting, including examples like Mahjong, Poker, and various board and vide…
Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift
Jiawei Ge, Shange Tang, Jianqing Fan +2
A key challenge of modern machine learning systems is to achieve Out-of-Distribution (OOD) generalization -- generalizing to target data whose distribution differs from that of sou…