Eric Zhao

According to our database, Eric Zhao authored at least 17 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

The Limits of Preference Data for Post-Training.

Eric Zhao, Jessica Dai, Pranjal Awasthi

CoRR, May, 2025

From Style to Facts: Mapping the Boundaries of Knowledge Injection with Finetuning.

Eric Zhao, Pranjal Awasthi, Nika Haghtalab

CoRR, March, 2025

Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification.

Eric Zhao, Pranjal Awasthi, Sreenivas Gollapudi

CoRR, February, 2025

Truthfulness of Decision-Theoretic Calibration Measures.

Mingda Qiao, Eric Zhao

Proceedings of the Thirty Eighth Annual Conference on Learning Theory, 2025

2024

Learning Variational Inequalities from Data: Fast Generalization Rates under Strong Monotonicity.

Eric Zhao, Tatjana Chavdarova, Michael I. Jordan

CoRR, 2024

Learning With Multi-Group Guarantees For Clusterable Subpopulations.

Jessica Dai, Nika Haghtalab, Eric Zhao

CoRR, 2024

Algorithmic Content Selection and the Impact of User Disengagement.

Emilio Calvano, Nika Haghtalab, Ellen Vitercik, Eric Zhao

CoRR, 2024

Stacking as Accelerated Gradient Descent.

Naman Agarwal, Pranjal Awasthi, Satyen Kale, Eric Zhao

CoRR, 2024

Truthfulness of Calibration Measures.

Nika Haghtalab, Mingda Qiao, Kunhe Yang, Eric Zhao

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Semantic Routing via Autoregressive Modeling.

Eric Zhao, Pranjal Awasthi, Zhengdao Chen, Sreenivas Gollapudi, Daniel Delling

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

The Sample Complexity of Multi-Distribution Learning for VC Classes.

Pranjal Awasthi, Nika Haghtalab, Eric Zhao

CoRR, 2023

A Unifying Perspective on Multi-Calibration: Unleashing Game Dynamics for Multi-Objective Learning.

Nika Haghtalab, Michael I. Jordan, Eric Zhao

CoRR, 2023

A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning.

Nika Haghtalab, Michael I. Jordan, Eric Zhao

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Open Problem: The Sample Complexity of Multi-Distribution Learning for VC Classes.

Pranjal Awasthi, Nika Haghtalab, Eric Zhao

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Learning to Play General-Sum Games against Multiple Boundedly Rational Agents.

Eric Zhao, Alexander R. Trott, Caiming Xiong, Stephan Zheng

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

On-Demand Sampling: Learning Optimally from Multiple Distributions.

Nika Haghtalab, Michael I. Jordan, Eric Zhao

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

ERMAS: Becoming Robust to Reward Function Sim-to-Real Gaps in Multi-Agent Simulations.

Eric Zhao, Alexander R. Trott, Caiming Xiong, Stephan Zheng

CoRR, 2021