74 citations · 382 across the 26 of their papers we have counts for
48 papers
Reinforcement Learning with Stepwise Fairness Constraints
Zhun Deng, He Sun, Zhiwei Steven Wu +2
AI methods are used in societally important settings, ranging from credit to employment to housing, and it is crucial to provide fairness in regard to algorithmic decision making.…
Private Synthetic Data for Multitask Learning and Marginal Queries
Giuseppe Vietri, Cedric Archambeau, Sergul Aydore +6
We provide a differentially private algorithm for producing synthetic data simultaneously useful for multiple tasks: marginal queries and multitask machine learning (ML). A key inn…
Meta-Learning Adversarial Bandits
Maria-Florina Balcan, Keegan Harris, Mikhail Khodak +1
We study online learning with bandit feedback across multiple tasks, with the goal of improving average performance across tasks if they are similar according to some natural task-…
Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders
Logan Stapleton, Min Hun Lee, Diana Qing +5
Child welfare agencies across the United States are turning to data-driven predictive technologies (commonly called predictive analytics) which use government administrative data t…
Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support
Anna Kawakami, Venkatesh Sivaraman, Hao-Fei Cheng +7
AI-based decision support tools (ADS) are increasingly used to augment human decision-making in high-stakes, social contexts. As public sector agencies begin to adopt ADS, it is cr…
Causal Imitation Learning under Temporally Correlated Noise
Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell +1
We develop algorithms for imitation learning from policy data that was corrupted by temporally correlated noise in expert actions. When noise affects multiple timesteps of recorded…