Lydia T. Liu

According to our database, Lydia T. Liu authored at least 15 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

On the Actionability of Outcome Prediction.

Lydia T. Liu, Solon Barocas, Jon M. Kleinberg, Karen Levy

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2022

Social Dynamics of Machine Learning for Decision Making

Lydia T. Liu

PhD thesis, 2022

Lost in Translation: Reimagining the Machine Learning Life Cycle in Education.

Lydia T. Liu, Serena Lutong Wang, Tolani Britton, Rediet Abebe

CoRR, 2022

Strategic ranking.

Lydia T. Liu, Nikhil Garg, Christian Borgs

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Bandit Learning in Decentralized Matching Markets.

Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan

J. Mach. Learn. Res., 2021

2020

Steerable ePCA: Rotationally Invariant Exponential Family PCA.

Zhizhen Zhao, Lydia T. Liu, Amit Singer

IEEE Trans. Image Process., 2020

Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning.

Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T. Liu, Daniel Björkegren, Moritz Hardt, Joshua Blumenstock

Proceedings of the 37th International Conference on Machine Learning, 2020

The disparate equilibria of algorithmic decision making when individuals invest rationally.

Lydia T. Liu, Ashia Wilson, Nika Haghtalab, Adam Tauman Kalai, Christian Borgs, Jennifer T. Chayes

Proceedings of the FAT* '20: Conference on Fairness, 2020

Competing Bandits in Matching Markets.

Lydia T. Liu, Horia Mania, Michael I. Jordan

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

The Implicit Fairness Criterion of Unconstrained Learning.

Lydia T. Liu, Max Simchowitz, Moritz Hardt

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Steerable ePCA.

Zhizhen Zhao, Lydia T. Liu, Amit Singer

CoRR, 2018

Group calibration is a byproduct of unconstrained learning.

Lydia T. Liu, Max Simchowitz, Moritz Hardt

CoRR, 2018

Minimizing Nonconvex Population Risk from Rough Empirical Risk.

Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan

CoRR, 2018

On the Local Minima of the Empirical Risk.

Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Delayed Impact of Fair Machine Learning.

Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt

Proceedings of the 35th International Conference on Machine Learning, 2018