94 citations · 270 across the 17 of their papers we have counts for
8 papers · 1 filter
Reward Reports for Reinforcement Learning
Thomas Krendl Gilbert, Nathan Lambert, Sarah Dean +2
Building systems that are good for society in the face of complex societal effects requires a dynamic approach. Recent approaches to machine learning (ML) documentation have demons…
Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning Systems
Thomas Krendl Gilbert, Sarah Dean, Tom Zick +1
In the long term, reinforcement learning (RL) is considered by many AI theorists to be the most promising path to artificial general intelligence. This places RL practitioners in a…
Certainty Equivalent Perception-Based Control
Sarah Dean, Benjamin Recht
In order to certify performance and safety, feedback control requires precise characterization of sensor errors. In this paper, we provide guarantees on such feedback systems when…
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…
Recommendations and User Agency: The Reachability of Collaboratively-Filtered Information
Sarah Dean, Sarah Rich, Benjamin Recht
Recommender systems often rely on models which are trained to maximize accuracy in predicting user preferences. When the systems are deployed, these models determine the availabili…
A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics
Roel Dobbe, Sarah Dean, Thomas Gilbert +1
Machine learning (ML) is increasingly deployed in real world contexts, supplying actionable insights and forming the basis of automated decision-making systems. While issues result…
Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator
Sarah Dean, Horia Mania, Nikolai Matni +2
We consider adaptive control of the Linear Quadratic Regulator (LQR), where an unknown linear system is controlled subject to quadratic costs. Leveraging recent developments in the…
Delayed Impact of Fair Machine Learning
Lydia T. Liu, Sarah Dean, Esther Rolf +2
Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventi…