Joshua Cohen and Lydia T. Liu.
The Reach of Fairness.
Manuscript, in submission.
Inioluwa Deborah Raji and Lydia T. Liu.
Designing Experimental Evaluations of Algorithmic Interventions with Human Decision Makers In Mind.
Manuscript, in submission.
A preliminary version was presented as a poster at the ICML 2024 workshop on Humans, Algorithmic Decision-Making and Society.
Lydia T. Liu*, Serena Wang*, Tolani Britton^, Rediet Abebe^.
Reimagining the Machine Learning Life Cycle to Improve Educational Outcomes of Students.
Proceedings of the National Academy of Sciences 120.9 (2023): e2204781120. [eprint] [slides]
A preliminary version (“Promises and Pitfalls of Machine Learning in Education”) was presented as a poster at the Research Conference on Communications, Information, and Internet Policy (TPRC 2021).
Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan.
Bandit Learning in Decentralized Matching Markets.
Journal of Machine Learning Research, 22(211):1−34, 2021. [journal] [arxiv]
Presented as a poster at Workshop on Operations of People-Centric Systems (EC ‘21)
Zhizhen Zhao, Lydia T. Liu, Amit Singer.
Steerable ePCA: Rotationally Invariant Exponential Family PCA.
IEEE Transactions on Image Processing, vol. 29, pp. 6069-6081, 2020. [doi] [arxiv]
Lydia T. Liu*, Edgar Dobriban*, and Amit Singer.
ePCA: High Dimensional Exponential Family PCA.
Annals of Applied Statistics 2018, Vol. 12, No. 4, 2121-2150. [doi] [arxiv] [software]
Lydia T. Liu, Solon Barocas, Jon Kleinberg, Karen Levy.
On the Actionability of Outcome Prediction.
Proceedings of the AAAI conference on Artificial Intelligence, 2024. [doi] [arxiv]
Lydia T. Liu, Nikhil Garg, Christian Borgs.
Strategic ranking.
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 2022. [arxiv]
Presented as a poster at the ACM conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO), 2021.
Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T. Liu, Daniel Björkegren, Moritz Hardt, Joshua Blumenstock.
Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning.
Proceedings of the 37th International Conference on Machine Learning (ICML), 2020. [arxiv]
Lydia T. Liu*, Horia Mania*, Michael I. Jordan.
Competing Bandits in Matching Markets.
Proceedings of The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. [arxiv]
Lydia T. Liu, Ashia Wilson, Nika Haghtalab, Adam Tauman Kalai, Christian Borgs, Jennifer Chayes.
The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationally.
Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (ACM FAT*), Barcelona, Spain, 2020. [doi]
[arxiv]
Lydia T. Liu*, Max Simchowitz*, Moritz Hardt.
The Implicit Fairness Criterion of Unconstrained Learning.
Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, California, USA, 2019. [arxiv] [code]
Chi Jin*, Lydia T. Liu*, Rong Ge, Michael I. Jordan.
On the Local Minima of the Empirical Risk.
Advances in Neural Information Processing Systems (NeurIPS) 32, Montréal, Canada, 2018. Spotlight. [arxiv]
Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt.
Delayed Impact of Fair Machine Learning.
Proceedings of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018. Best Paper Award. [arxiv] [code]
Yuhan Liu, Yuhan Zheng, Siyuan Zhang, Lydia T. Liu.
Evaluating Fairness in Black-box Algorithmic Markets: A Case Study of Ride Sharing in Chicago.
International Conference on Machine Learning (ICML) Workshop on Humans, Algorithmic Decision-Making and Society: Modeling Interactions and Impact.
[arxiv]
Benedikt Stroebl, Rajiv Krishna Swamy, Lydia T. Liu.
A Baseline that Matters: Categorical Prioritization as Benchmark for Social Policy Algorithms.
International Conference on Machine Learning (ICML) Workshop on Humans, Algorithmic Decision-Making and Society: Modeling Interactions and Impact.
[poster]
Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T. Liu, Daniel Björkegren, Moritz Hardt, Joshua Blumenstock.
Balancing Competing Objectives for Welfare-Aware Machine Learning with Imperfect Data.
NeurIPS Joint Workshop on AI for Social Good, Vancouver, Canada, 2019. Best Paper Award.
Lydia T. Liu, Urun Dogan, and Katja Hofmann.
Decoding multitask DQN in the world of Minecraft.
The 13th European Workshop on Reinforcement Learning, Barcelona, Spain, 2016. Also presented at the 11th Women in Machine Learning Workshop and the Deep Reinforcement Learning Workshop at NeurIPS 2016.
* ^ equal contribution
On the Actionability of Outcome Prediction. Montreal AI Ethics Institute, AI Ethics Brief. Feb 2024.
When bias begets bias: A source of negative feedback loops in AI systems. Microsoft Research Blog. Jan 2020.
Delayed Impact of Fair Machine Learning. Co-authored with Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt. Berkeley AI Research Blog. May 2018.
BIRS workshop on Bridging Prediction and Intervention Problems in Social Systems, June 3-7, 2024. Banff, AB, Canada.
AAAI 2024 Workshop on AI for Education: Bridging Innovation and Responsibility, Feb 26-27, 2024. Vancouver, BC, Canada.
NeurIPS 2020 Workshop on Consequential Decision Making in Dynamic Environments. Virtual.
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