no code implementations • ICML 2020 • Giuseppe Vietri, Borja de Balle Pigem, Steven Wu, Akshay Krishnamurthy
Motivated by high-stakes decision-making domains like personalized medicine where user information is inherently sensitive, we design privacy preserving exploration policies for episodic reinforcement learning (RL).
no code implementations • 13 Feb 2024 • Adam Block, Mark Bun, Rathin Desai, Abhishek Shetty, Steven Wu
Due to statistical lower bounds on the learnability of many function classes under privacy constraints, there has been recent interest in leveraging public data to improve the performance of private learning algorithms.
no code implementations • 21 Apr 2022 • Nil-Jana Akpinar, Manish Nagireddy, Logan Stapleton, Hao-Fei Cheng, Haiyi Zhu, Steven Wu, Hoda Heidari
This stylized setup offers the distinct capability of testing fairness interventions beyond observational data and against an unbiased benchmark.
no code implementations • 3 Feb 2022 • Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, Himabindu Lakkaraju
To this end, we first conduct interviews with data scientists to understand what constitutes disagreement between explanations generated by different methods for the same model prediction, and introduce a novel quantitative framework to formalize this understanding.
no code implementations • 29 Sep 2021 • Gokul Swamy, Sanjiban Choudhury, Drew Bagnell, Steven Wu
Both approaches are able to find policies that match the result of a query to an unconfounded expert.