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ORCIDAngela Zhou
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2020 – today
- 2024
[c15]Angela Zhou:
Reward-Relevance-Filtered Linear Offline Reinforcement Learning. AISTATS 2024: 3025-3033- [i22]Angela Zhou:
Reward-Relevance-Filtered Linear Offline Reinforcement Learning. CoRR abs/2401.12934 (2024) - [i21]Ezinne Nwankwo, Michael I. Jordan, Angela Zhou:
Reduced-Rank Multi-objective Policy Learning and Optimization. CoRR abs/2404.18490 (2024) - [i20]Christoph Kern, Michael Kim, Angela Zhou:
Multi-CATE: Multi-Accurate Conditional Average Treatment Effect Estimation Robust to Unknown Covariate Shifts. CoRR abs/2405.18206 (2024) - [i19]Angela Zhou:
Orthogonalized Estimation of Difference of Q-functions. CoRR abs/2406.08697 (2024) - 2023
- [c14]Angela Zhou:
Optimal and Fair Encouragement Policy Evaluation and Learning. NeurIPS 2023 - [i18]David Bruns-Smith, Angela Zhou:
Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounders. CoRR abs/2302.00662 (2023) - [i17]Angela Zhou:
Optimal and Fair Encouragement Policy Evaluation and Learning. CoRR abs/2309.07176 (2023) - 2022
- [j2]Nathan Kallus
, Xiaojie Mao, Angela Zhou:
Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination. Manag. Sci. 68(3): 1959-1981 (2022) - [c13]Nathan Kallus, Angela Zhou:
Stateful Offline Contextual Policy Evaluation and Learning. AISTATS 2022: 11169-11194 - [c12]Wenshuo Guo, Michael I. Jordan, Angela Zhou:
Off-Policy Evaluation with Policy-Dependent Optimization Response. NeurIPS 2022 - [c11]Michael I. Jordan, Yixin Wang, Angela Zhou:
Empirical Gateaux Derivatives for Causal Inference. NeurIPS 2022 - [i16]Wenshuo Guo, Michael I. Jordan, Angela Zhou:
Off-Policy Evaluation with Policy-Dependent Optimization Response. CoRR abs/2202.12958 (2022) - [i15]Michael I. Jordan, Yixin Wang, Angela Zhou:
Empirical Gateaux Derivatives for Causal Inference. CoRR abs/2208.13701 (2022) - [i14]Connor Lawless, Angela Zhou:
A Note on Task-Aware Loss via Reweighing Prediction Loss by Decision-Regret. CoRR abs/2211.05116 (2022) - 2021
- [j1]Nathan Kallus, Angela Zhou:
Minimax-Optimal Policy Learning Under Unobserved Confounding. Manag. Sci. 67(5): 2870-2890 (2021) - [c10]Nathan Kallus, Angela Zhou:
Fairness, Welfare, and Equity in Personalized Pricing. FAccT 2021: 296-314 - [c9]Michelle Bao, Angela Zhou, Samantha Zottola, Brian Brubach, Sarah Desmarais, Aaron Horowitz, Kristian Lum, Suresh Venkatasubramanian:
It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks. NeurIPS Datasets and Benchmarks 2021 - [i13]Michelle Bao, Angela Zhou, Samantha Zottola, Brian Brubach, Sarah Desmarais, Aaron Horowitz, Kristian Lum, Suresh Venkatasubramanian:
It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks. CoRR abs/2106.05498 (2021) - [i12]Nathan Kallus, Angela Zhou:
Stateful Offline Contextual Policy Evaluation and Learning. CoRR abs/2110.10081 (2021) - [i11]Angela Zhou, Andrew Koo, Nathan Kallus, Rene Ropac, Richard Peterson, Stephen Koppel, Tiffany Bergin:
An Empirical Evaluation of the Impact of New York's Bail Reform on Crime Using Synthetic Controls. CoRR abs/2111.08664 (2021) - 2020
- [c8]Nathan Kallus, Xiaojie Mao, Angela Zhou:
Assessing algorithmic fairness with unobserved protected class using data combination. FAT* 2020: 110 - [c7]Nathan Kallus, Angela Zhou:
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning. NeurIPS 2020 - [i10]Nathan Kallus, Angela Zhou:
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning. CoRR abs/2002.04518 (2020) - [i9]Nathan Kallus, Angela Zhou:
Fairness, Welfare, and Equity in Personalized Pricing. CoRR abs/2012.11066 (2020)
2010 – 2019
- 2019
- [c6]Nathan Kallus, Xiaojie Mao, Angela Zhou:
Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding. AISTATS 2019: 2281-2290 - [c5]Nathan Kallus, Angela Zhou:
Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds. NeurIPS 2019: 3421-3432 - [c4]Nathan Kallus, Angela Zhou:
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric. NeurIPS 2019: 3433-3443 - [i8]Nathan Kallus, Angela Zhou:
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric. CoRR abs/1902.05826 (2019) - [i7]Nathan Kallus, Xiaojie Mao, Angela Zhou:
Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination. CoRR abs/1906.00285 (2019) - [i6]Nathan Kallus, Angela Zhou:
Assessing Disparate Impacts of Personalized Interventions: Identifiability and Bounds. CoRR abs/1906.01552 (2019) - 2018
- [c3]Nathan Kallus, Angela Zhou:
Policy Evaluation and Optimization with Continuous Treatments. AISTATS 2018: 1243-1251 - [c2]Nathan Kallus, Angela Zhou:
Residual Unfairness in Fair Machine Learning from Prejudiced Data. ICML 2018: 2444-2453 - [c1]Nathan Kallus, Angela Zhou:
Confounding-Robust Policy Improvement. NeurIPS 2018: 9289-9299 - [i5]Nathan Kallus, Angela Zhou:
Policy Evaluation and Optimization with Continuous Treatments. CoRR abs/1802.06037 (2018) - [i4]Nathan Kallus, Angela Zhou:
Confounding-Robust Policy Improvement. CoRR abs/1805.08593 (2018) - [i3]Nathan Kallus, Angela Zhou:
Residual Unfairness in Fair Machine Learning from Prejudiced Data. CoRR abs/1806.02887 (2018) - [i2]Nathan Kallus, Xiaojie Mao, Angela Zhou:
Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding. CoRR abs/1810.02894 (2018) - 2017
- [i1]Irineo Cabreros, Karan Singh, Angela Zhou:
A Mixture Model and Task Allocation Scheme for Crowdsourcing. CoRR abs/1701.08795 (2017)
Coauthor Index
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last updated on 2024-07-25 20:19 CEST by the dblp team
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