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07/10/2023
Ranking with Long-Term Constraints
The feedback that users provide through their choices (e.g., clicks, pur...
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03/23/2023
Decision-aid or Controller? Steering Human Decision Makers with Algorithms
Algorithms are used to aid human decision makers by making predictions a...
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12/22/2022
Cross-Dataset Propensity Estimation for Debiasing Recommender Systems
Datasets for training recommender systems are often subject to distribut...
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10/18/2022
Online Convex Optimization with Unbounded Memory
Online convex optimization (OCO) is a widely used framework in online le...
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06/27/2022
Modeling Content Creator Incentives on Algorithm-Curated Platforms
Content creators compete for user attention. Their reach crucially depen...
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06/06/2022
Multi-learner risk reduction under endogenous participation dynamics
Prediction systems face exogenous and endogenous distribution shift – th...
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05/25/2022
Preference Dynamics Under Personalized Recommendations
Many projects (both practical and academic) have designed algorithms to ...
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04/22/2022
Reward Reports for Reinforcement Learning
The desire to build good systems in the face of complex societal effects...
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02/11/2022
Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning Systems
In the long term, reinforcement learning (RL) is considered by many AI t...
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06/30/2021
Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability
In this work, we consider how preference models in interactive recommend...
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04/26/2021
Axes for Sociotechnical Inquiry in AI Research
The development of artificial intelligence (AI) technologies has far exc...
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02/04/2021
AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks
Despite interest in communicating ethical problems and social contexts w...
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11/21/2020
Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty
Modern nonlinear control theory seeks to endow systems with properties s...
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11/07/2020
Do Offline Metrics Predict Online Performance in Recommender Systems?
Recommender systems operate in an inherently dynamical setting. Past rec...
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10/30/2020
Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions
Modern nonlinear control theory seeks to develop feedback controllers th...
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08/27/2020
Certainty Equivalent Perception-Based Control
In order to certify performance and safety, feedback control requires pr...
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03/15/2020
Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning
While real-world decisions involve many competing objectives, algorithmi...
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12/20/2019
Recommendations and User Agency: The Reachability of Collaboratively-Filtered Information
Recommender systems often rely on models which are trained to maximize a...
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07/08/2019
Robust Guarantees for Perception-Based Control
Motivated by vision based control of autonomous vehicles, we consider th...
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09/26/2018
Safely Learning to Control the Constrained Linear Quadratic Regulator
We study the constrained linear quadratic regulator with unknown dynamic...
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07/02/2018
A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics
Machine learning (ML) is increasingly deployed in real world contexts, s...
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05/23/2018
Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator
We consider adaptive control of the Linear Quadratic Regulator (LQR), wh...
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03/12/2018
Delayed Impact of Fair Machine Learning
Fairness in machine learning has predominantly been studied in static cl...
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10/04/2017
