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ORCIDLydia T. Liu
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2020 – today
- 2024
[c9]Lydia T. Liu, Solon Barocas, Jon M. Kleinberg, Karen Levy:
On the Actionability of Outcome Prediction. AAAI 2024: 22240-22249- [i12]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 abs/2407.20522 (2024) - 2023
- [i11]Lydia T. Liu, Solon Barocas, Jon M. Kleinberg, Karen Levy
:
On the Actionability of Outcome Prediction. CoRR abs/2309.04470 (2023) - 2022
- [b1]Lydia T. Liu:
Social Dynamics of Machine Learning for Decision Making. University of California, Berkeley, USA, 2022 - [c8]Lydia T. Liu, Nikhil Garg, Christian Borgs:
Strategic ranking. AISTATS 2022: 2489-2518 - [i10]Lydia T. Liu, Serena Lutong Wang, Tolani Britton, Rediet Abebe:
Lost in Translation: Reimagining the Machine Learning Life Cycle in Education. CoRR abs/2209.03929 (2022) - 2021
- [j2]Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan:
Bandit Learning in Decentralized Matching Markets. J. Mach. Learn. Res. 22: 211:1-211:34 (2021) - [i9]Lydia T. Liu, Nikhil Garg, Christian Borgs:
Strategic Ranking. CoRR abs/2109.08240 (2021) - 2020
- [j1]Zhizhen Zhao, Lydia T. Liu, Amit Singer:
Steerable ePCA: Rotationally Invariant Exponential Family PCA. IEEE Trans. Image Process. 29: 6069-6081 (2020) - [c7]Lydia T. Liu, Horia Mania, Michael I. Jordan:
Competing Bandits in Matching Markets. AISTATS 2020: 1618-1628 - [c6]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: 381-391 - [c5]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: 8158-8168 - [i8]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 abs/2003.06740 (2020) - [i7]Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan:
Bandit Learning in Decentralized Matching Markets. CoRR abs/2012.07348 (2020)
2010 – 2019
- 2019
- [c4]Lydia T. Liu, Max Simchowitz, Moritz Hardt:
The Implicit Fairness Criterion of Unconstrained Learning. ICML 2019: 4051-4060 - [c3]Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt:
Delayed Impact of Fair Machine Learning. IJCAI 2019: 6196-6200 - [i6]Lydia T. Liu, Horia Mania, Michael I. Jordan:
Competing Bandits in Matching Markets. CoRR abs/1906.05363 (2019) - [i5]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 abs/1910.04123 (2019) - 2018
- [c2]Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt:
Delayed Impact of Fair Machine Learning. ICML 2018: 3156-3164 - [c1]Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan:
On the Local Minima of the Empirical Risk. NeurIPS 2018: 4901-4910 - [i4]Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt:
Delayed Impact of Fair Machine Learning. CoRR abs/1803.04383 (2018) - [i3]Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan:
Minimizing Nonconvex Population Risk from Rough Empirical Risk. CoRR abs/1803.09357 (2018) - [i2]Lydia T. Liu, Max Simchowitz, Moritz Hardt:
Group calibration is a byproduct of unconstrained learning. CoRR abs/1808.10013 (2018) - [i1]Zhizhen Zhao, Lydia T. Liu, Amit Singer:
Steerable ePCA. CoRR abs/1812.08789 (2018)
Coauthor Index
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