Eric Zhao

23 papers A* 9Journal 14
YearRankTypeTitle / Venue / Authors
2025 J jnl
From Style to Facts: Mapping the Boundaries of Knowledge Injection with Finetuning.
CoRR
Eric Zhao, Pranjal Awasthi, Nika Haghtalab
2025 A* conf
Learning With Multi-Group Guarantees For Clusterable Subpopulations.
ICML
Jessica Dai, Nika Haghtalab, Eric Zhao
2025 J jnl
Panprediction: Optimal Predictions for Any Downstream Task and Loss.
CoRR
Sivaraman Balakrishnan, Nika Haghtalab, Daniel Hsu, Brian Lee, Eric Zhao
2025 A* conf
Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification.
ICML
Eric Zhao, Pranjal Awasthi, Sreenivas Gollapudi
2025 J jnl
Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification.
CoRR
Eric Zhao, Pranjal Awasthi, Sreenivas Gollapudi
2025 J jnl
The Limits of Preference Data for Post-Training.
CoRR
Eric Zhao, Jessica Dai, Pranjal Awasthi
2025 A* conf
Truthfulness of Decision-Theoretic Calibration Measures.
COLT
Mingda Qiao, Eric Zhao
2025 J jnl
Truthfulness of Decision-Theoretic Calibration Measures.
CoRR
Mingda Qiao, Eric Zhao
2024 J jnl
Algorithmic Content Selection and the Impact of User Disengagement.
CoRR
Emilio Calvano, Nika Haghtalab, Ellen Vitercik, Eric Zhao
2024 J jnl
Learning Variational Inequalities from Data: Fast Generalization Rates under Strong Monotonicity.
CoRR
Eric Zhao, Tatjana Chavdarova, Michael I. Jordan
2024 J jnl
Learning With Multi-Group Guarantees For Clusterable Subpopulations.
CoRR
Jessica Dai, Nika Haghtalab, Eric Zhao
2024 A* conf
Semantic Routing via Autoregressive Modeling.
NeurIPS
Eric Zhao, Pranjal Awasthi, Zhengdao Chen, Sreenivas Gollapudi, Daniel Delling
2024 J jnl
Stacking as Accelerated Gradient Descent.
CoRR
Naman Agarwal, Pranjal Awasthi, Satyen Kale, Eric Zhao
2024 A* conf
Truthfulness of Calibration Measures.
NeurIPS
Nika Haghtalab, Mingda Qiao, Kunhe Yang, Eric Zhao
2024 J jnl
Truthfulness of Calibration Measures.
CoRR
Nika Haghtalab, Mingda Qiao, Kunhe Yang, Eric Zhao
2023 A* conf
A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning.
NeurIPS
Nika Haghtalab, Michael I. Jordan, Eric Zhao
2023 J jnl
A Unifying Perspective on Multi-Calibration: Unleashing Game Dynamics for Multi-Objective Learning.
CoRR
Nika Haghtalab, Michael I. Jordan, Eric Zhao
2023 A* conf
Learning to Play General-Sum Games against Multiple Boundedly Rational Agents.
AAAI
Eric Zhao, Alexander R. Trott, Caiming Xiong, Stephan Zheng
2023 A* conf
Open Problem: The Sample Complexity of Multi-Distribution Learning for VC Classes.
COLT
Pranjal Awasthi, Nika Haghtalab, Eric Zhao
2023 J jnl
The Sample Complexity of Multi-Distribution Learning for VC Classes.
CoRR
Pranjal Awasthi, Nika Haghtalab, Eric Zhao
2022 A* conf
On-Demand Sampling: Learning Optimally from Multiple Distributions.
NeurIPS
Nika Haghtalab, Michael I. Jordan, Eric Zhao
2022 J jnl
On-Demand Sampling: Learning Optimally from Multiple Distributions.
CoRR
Nika Haghtalab, Michael I. Jordan, Eric Zhao
2021 J jnl
ERMAS: Becoming Robust to Reward Function Sim-to-Real Gaps in Multi-Agent Simulations.
CoRR
Eric Zhao, Alexander R. Trott, Caiming Xiong, Stephan Zheng