Lydia T. Liu*, Horia Mania*, Michael I. Jordan.
Competing Bandits in Matching Markets.
Proceedings of The 23nd International Conference on Artificial Intelligence and Statistics (AISTATS), Palermo, Sicily, Italy, 2020. To appear. [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* 2020), Barcelona, Spain. To Appear. [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]
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.
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.
Our work has been featured in: