1 citations · 1 across the 2 of their papers we have counts for
8 papers
Lost in Translation: Reimagining the Machine Learning Life Cycle in Education
Lydia T. Liu, Serena Wang, Tolani Britton +1
Machine learning (ML) techniques are increasingly prevalent in education, from their use in predicting student dropout, to assisting in university admissions, and facilitating the…
Bandit Learning in Decentralized Matching Markets
Lydia T. Liu, Feng Ruan, Horia Mania +1
We study two-sided matching markets in which one side of the market (the players) does not have a priori knowledge about its preferences for the other side (the arms) and is requir…
Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning
Esther Rolf, Max Simchowitz, Sarah Dean +4
While real-world decisions involve many competing objectives, algorithmic decisions are often evaluated with a single objective function. In this paper, we study algorithmic polici…
The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationally
Lydia T. Liu, Ashia Wilson, Nika Haghtalab +3
The long-term impact of algorithmic decision making is shaped by the dynamics between the deployed decision rule and individuals' response. Focusing on settings where each individu…
Competing Bandits in Matching Markets
Lydia T. Liu, Horia Mania, Michael I. Jordan
Stable matching, a classical model for two-sided markets, has long been studied with little consideration for how each side's preferences are learned. With the advent of massive on…
The implicit fairness criterion of unconstrained learning
Lydia T. Liu, Max Simchowitz, Moritz Hardt
We clarify what fairness guarantees we can and cannot expect to follow from unconstrained machine learning. Specifically, we characterize when unconstrained learning on its own imp…