Lydia T. Liu

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 23

Top Topics

Rotationally Invariant

Machine Learning

Outcome Prediction

Learning From Noisy Data

Top Venues

IEEE Trans. Image Process.

Publications

Yuhan Liu, Yuhan Zheng, Siyuan Zhang, Lydia T. Liu
Evaluating Fairness in Black-box Algorithmic Markets: A Case Study of Ride Sharing in Chicago. CoRR (2024)
Lydia T. Liu, Solon Barocas, Jon M. Kleinberg, Karen Levy
On the Actionability of Outcome Prediction. AAAI (2024)
Lydia T. Liu, Solon Barocas, Jon M. Kleinberg, Karen Levy
On the Actionability of Outcome Prediction. CoRR (2023)
Lydia T. Liu, Nikhil Garg, Christian Borgs
Strategic ranking. AISTATS (2022)
Lydia T. Liu, Serena Wang, Tolani Britton, Rediet Abebe
Lost in Translation: Reimagining the Machine Learning Life Cycle in Education. CoRR (2022)
Lydia T. Liu, Nikhil Garg, Christian Borgs
Strategic Ranking. CoRR (2021)
Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan
Bandit Learning in Decentralized Matching Markets. J. Mach. Learn. Res. 22 (2021)
Zhizhen Zhao, Lydia T. Liu, Amit Singer
Steerable ePCA: Rotationally Invariant Exponential Family PCA. IEEE Trans. Image Process. 29 (2020)
Lydia T. Liu, Ashia Wilson, Nika Haghtalab, Adam Tauman Kalai, Christian Borgs, Jennifer T. Chayes
The disparate equilibria of algorithmic decision making when individuals invest rationally. FAT* (2020)
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. ICML (2020)
Lydia T. Liu, Horia Mania, Michael I. Jordan
Competing Bandits in Matching Markets. AISTATS (2020)
Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan
Bandit Learning in Decentralized Matching Markets. CoRR (2020)
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. CoRR (2020)
Lydia T. Liu, Ashia Wilson, Nika Haghtalab, Adam Tauman Kalai, Christian Borgs, Jennifer T. Chayes
The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationally. CoRR (2019)
Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt
Delayed Impact of Fair Machine Learning. IJCAI (2019)
Lydia T. Liu, Max Simchowitz, Moritz Hardt
The Implicit Fairness Criterion of Unconstrained Learning. ICML (2019)
Lydia T. Liu, Horia Mania, Michael I. Jordan
Competing Bandits in Matching Markets. CoRR (2019)
Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan
Minimizing Nonconvex Population Risk from Rough Empirical Risk. CoRR (2018)
Lydia T. Liu, Max Simchowitz, Moritz Hardt
Group calibration is a byproduct of unconstrained learning. CoRR (2018)
Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt
Delayed Impact of Fair Machine Learning. ICML (2018)
Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt
Delayed Impact of Fair Machine Learning. CoRR (2018)
Zhizhen Zhao, Lydia T. Liu, Amit Singer
Steerable ePCA. CoRR (2018)
Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan
On the Local Minima of the Empirical Risk. NeurIPS (2018)