Eric Zhao

Papers

(α-β) denotes when authors are ordered alphabetically.

On Language Models

From Style to Facts: Mapping the Boundaries of Knowledge Injection with Finetuning
Preprint, Feb 2025
Eric Zhao, Pranjal Awasthi, Nika Haghtalab
@misc{stylefacts_2025, title = {From Style to Facts: Mapping the Boundaries of Knowledge Injection with Finetuning}, author = {Zhao, Eric and Awasthi, Pranjal and Haghtalab, Nika}, month = {Feb}, year = {2025}, note = {Preprint}, pdf = {https://eric-zhao.com/files/finetuning_spectrum.pdf} }
Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification
Preprint, Jan 2025
Eric Zhao, Pranjal Awasthi, Sreenivas Gollapudi
@misc{samplingsearch_2025, title = {Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification}, author = {Zhao, Eric and Awasthi, Pranjal and Gollapudi, Sreenivas}, month = {Jan}, year = {2025}, note = {Preprint}, arxiv = {2502.01839}, pdf = {https://eric-zhao.com/files/sampling_based_search.pdf} }

On The Theory of Multi-Objective Learning

Learning With Multi-Group Guarantees For Clusterable Subpopulations
Preprint, Sept 2024
(α-β) Jessica Dai, Nika Haghtalab, Eric Zhao
@misc{multigroup_2024, title = {Learning With Multi-Group Guarantees For Clusterable Subpopulations}, author = {Dai, Jessica and Haghtalab, Nika and Zhao, Eric}, month = {Sept}, year = {2024}, note = {Preprint}, arxiv = {2410.14588}, pdf = {https://eric-zhao.com/files/multi_group_clusters.pdf} }
A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning
NeurIPS 2023
(α-β) Nika Haghtalab, Michael I. Jordan, Eric Zhao
@inproceedings{mc_2023, title = {A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning}, author = {Haghtalab, Nika and Jordan, Michael and Zhao, Eric}, booktitle = {Proceedings of the 37th Annual Conference on Neural Information Processing Systems}, month = {Feb}, year = {2023}, note = {Conference paper}, arxiv = {2302.10863}, pdf = {https://eric-zhao.com/files/Multi-Calibration%20Game%20Dynamics.pdf} }
The Sample Complexity of Multi-Distribution Learning for VC Classes
COLT 2023
(α-β) Pranjal Awasthi, Nika Haghtalab, Eric Zhao
@inproceedings{md_2023, title = {The Sample Complexity of Multi-Distribution Learning for VC Classes}, author = {Awasthi, Pranjal and Haghtalab, Nika and Zhao, Eric}, booktitle = {Proceedings of the 36th Annual Conference on Learning Theory}, month = {Jul}, year = {2023}, note = {Conference paper}, arxiv = {2307.12135}, pdf = {https://eric-zhao.com/files/openproblem.pdf} }
On-Demand Sampling: Learning Optimally from Multiple Distributions
NeurIPS 2022
Outstanding Paper Award (Top 0.5% of accepted papers)
(α-β) Nika Haghtalab, Michael I. Jordan, Eric Zhao
@inproceedings{zhao_ondemand_2022, title = {On-Demand Sampling: Learning Optimally from Multiple Distributions}, author = {Haghtalab, Nika and Jordan, Michael and Zhao, Eric}, booktitle = {Proceedings of the 36th Annual Conference on Neural Information Processing Systems}, month = {May}, year = {2022}, note = {Conference paper}, arxiv = {2210.12529}, pdf = {https://eric-zhao.com/files/On-Demand%20Sampling%20[Neurips%202022].pdf} }

On the Theory of Calibrated Forecasting

Truthfulness of Decision-Theoretic Calibration Measures
Preprint, Jan 2025
(α-β) Mingda Qiao, Eric Zhao
@misc{truthfuldt_2025, title = {Truthfulness of Decision-Theoretic Calibration Measures}, author = {Qiao, Mingda and Zhao, Eric}, month = {Jan}, year = {2025}, note = {Preprint}, pdf = {https://eric-zhao.com/files/decision_truthfulness.pdf} }
Truthfulness of Calibration Measures
NeurIPS 2024
(α-β) Nika Haghtalab, Mingda Qiao, Kunhe Yang, Eric Zhao
@inproceedings{calibration_2024, title = {Truthfulness of Calibration Measures}, author = {Haghtalab, Nika and Qiao, Mingda and Yang, Kunhe and Zhao, Eric}, booktitle = {Proceedings of the 38th Annual Conference on Neural Information Processing Systems}, month = {Feb}, year = {2024}, note = {Conference paper}, arxiv = {2407.13979}, pdf = {https://eric-zhao.com/files/truthfulness_of_calibration.pdf} }

On Microeconomics

Algorithmic Content Selection and the Impact of User Disengagement
Preprint, Feb 2024
(α-β) Emilio Calvano, Nika Haghtalab, Ellen Vitercik, Eric Zhao
@misc{elastic_2024, title = {Algorithmic Content Selection and the Impact of User Disengagement}, author = {Calvano, Emilio and Haghtalab, Nika and Vitercik, Ellen and Zhao, Eric}, month = {Feb}, year = {2024}, note = {Preprint}, arxiv = {2410.13108}, pdf = {https://eric-zhao.com/files/elastic_demand.pdf} }

On Optimization

Stacking as Accelerated Gradient Descent
Preprint, Feb 2024
(α-β) Naman Agarwal, Pranjal Awasthi, Satyen Kale, Eric Zhao
@misc{stacking_2024, title = {Stacking as Accelerated Gradient Descent}, author = {Agarwal, Naman and Awasthi, Pranjal and Kale, Satyen and Zhao, Eric}, month = {Feb}, year = {2024}, note = {Preprint}, arxiv = {2403.04978}, pdf = {https://eric-zhao.com/files/stacking.pdf} }
Learning Variational Inequalities from Data: Fast Generalization Rates under Strong Monotonicity
Technical report, Feb 2024
Eric Zhao, Tatjana Chavdarova, Michael Jordan
@techreport{vi_2024, title = {Learning Variational Inequalities from Data: Fast Generalization Rates under Strong Monotonicity}, author = {Zhao, Eric and Chavdarova, Tatjana and Jordan, Michael}, month = {Feb}, year = {2024}, institution = {}, note = {Technical report}, arxiv = {2410.20649}, pdf = {https://eric-zhao.com/files/vi_stability.pdf} }

On Domain Adaptation

Learning to Play General-Sum Games Against Multiple Boundedly Rational Agents
AAAI 2021
Eric Zhao, Alexander Trott, Caiming Xiong, Stephan Zheng
@inproceedings{zhao_learning_2021, title = {Learning to Play General-Sum Games Against Multiple Boundedly Rational Agents}, author = {Zhao, Eric and Trott, Alexander R. and Xiong, Caiming and Zheng, Stephan}, booktitle = {Proceedings of the 37th AAAI Conference on Artificial Intelligence}, month = {June}, year = {2021}, note = {Conference paper}, arxiv = {2106.05492}, pdf = {https://eric-zhao.com/files/robust_equilibria.pdf} }
Active Learning under Label Shift
AISTATS 2021
Eric Zhao, Anqi Liu, Animashree Anandkumar, Yisong Yue
@inproceedings{zhao_label_2021, title = {Active Learning under Label Shift}, author = {Zhao, Eric and Liu, Anqi and Anandkumar, Animashree and Yue, Yisong}, booktitle = {Proceedings of the 24th International Conference on Artificial Intelligence and Statistics}, month = {Feb}, year = {2021}, note = {Conference paper}, arxiv = {2007.08479}, pdf = {https://eric-zhao.com/files/label_shift.pdf} }

Misc.

Semantic Routing via Autoregressive Modeling
NeurIPS 2024
Eric Zhao, Pranjal Awasthi, Zhengdao Chen, Sreenivas Gollapudi, Daniel Delling
@inproceedings{routing_2024, title = {Semantic Routing via Autoregressive Modeling}, author = {Zhao, Eric and Awasthi, Pranjal and Chen, Zhengdao and Gollapudi, Sreenivas and Delling, Daniel}, booktitle = {Proceedings of the 38th Annual Conference on Neural Information Processing Systems}, month = {May}, year = {2024}, note = {Conference paper}, pdf = {https://eric-zhao.com/files/semantic_routing.pdf} }
Scaling Bias Mitigation with Multiple Fairness Tasks and Multiple Protected Attributes
Technical report, Sept 2021
Eric Zhao, De-An Huang, Hao Liu, Zhiding Yu, Anqi Liu, Olga Russakovsky, Anima Anandkumar
@techreport{ifas_2022, title = {Scaling Bias Mitigation with Multiple Fairness Tasks and Multiple Protected Attributes}, author = {Zhao, Eric and Huang, De-An and Liu, Hao and Yu, Zhiding and Liu, Anqi and Russakovsky, Olga and Anandkumar, Anima}, month = {May}, year = {2022}, institution = {}, note = {Technical report}, pdf = {https://eric-zhao.com/files/bias_mitigation.pdf} }