ERMAS: Becoming Robust to Reward Function Sim-to-Real Gaps in Multi-Agent Simulations. - Eric Zhao 003; Alexander R. Trott; Caiming Xiong; Stephan Zheng (CoRR, 2021)

On-Demand Sampling: Learning Optimally from Multiple Distributions. - Nika Haghtalab; Michael I. Jordan; Eric Zhao 003 (NeurIPS, 2022)

Learning to Play General-Sum Games against Multiple Boundedly Rational Agents. - Eric Zhao 003; Alexander R. Trott; Caiming Xiong; Stephan Zheng (AAAI, 2023)

Open Problem: The Sample Complexity of Multi-Distribution Learning for VC Classes. - Pranjal Awasthi; Nika Haghtalab; Eric Zhao 003 (COLT, 2023)

A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning. - Nika Haghtalab; Michael I. Jordan; Eric Zhao 003 (NeurIPS, 2023)

A Unifying Perspective on Multi-Calibration: Unleashing Game Dynamics for Multi-Objective Learning. - Nika Haghtalab; Michael I. Jordan; Eric Zhao 003 (CoRR, 2023)

The Sample Complexity of Multi-Distribution Learning for VC Classes. - Pranjal Awasthi; Nika Haghtalab; Eric Zhao 003 (CoRR, 2023)

Stacking as Accelerated Gradient Descent. - Naman Agarwal; Pranjal Awasthi; Satyen Kale; Eric Zhao 003 (CoRR, 2024)

Truthfulness of Calibration Measures. - Nika Haghtalab; Mingda Qiao; Kunhe Yang; Eric Zhao 003 (CoRR, 2024)