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06/09/2023
A Unified Model and Dimension for Interactive Estimation
We study an abstract framework for interactive learning called interacti...
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03/29/2023
Fairlearn: Assessing and Improving Fairness of AI Systems
Fairlearn is an open source project to help practitioners assess and imp...
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05/06/2022
Convex Analysis at Infinity: An Introduction to Astral Space
Not all convex functions on ℝ^n have finite minimizers; some can only be...
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02/10/2022
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization
Large-scale machine learning systems often involve data distributed acro...
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07/03/2021
Bayesian decision-making under misspecified priors with applications to meta-learning
Thompson sampling and other Bayesian sequential decision-making algorith...
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02/15/2021
Log-time Prediction Markets for Interval Securities
We design a prediction market to recover a complete and fully general pr...
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06/19/2020
Gradient descent follows the regularization path for general losses
Recent work across many machine learning disciplines has highlighted tha...
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06/09/2020
Constrained episodic reinforcement learning in concave-convex and knapsack settings
We propose an algorithm for tabular episodic reinforcement learning with...
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07/22/2019
Doubly robust off-policy evaluation with shrinkage
We design a new family of estimators for off-policy evaluation in contex...
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06/21/2019
Reinforcement Learning with Convex Constraints
In standard reinforcement learning (RL), a learning agent seeks to optim...
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05/30/2019
Fair Regression: Quantitative Definitions and Reduction-based Algorithms
In this paper, we study the prediction of a real-valued target, such as ...
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01/25/2019
Provably efficient RL with Rich Observations via Latent State Decoding
We study the exploration problem in episodic MDPs with rich observations...
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03/06/2018
A Reductions Approach to Fair Classification
We present a systematic approach for achieving fairness in a binary clas...
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03/03/2018
Practical Contextual Bandits with Regression Oracles
A major challenge in contextual bandits is to design general-purpose alg...
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03/01/2018
Hierarchical Imitation and Reinforcement Learning
We study the problem of learning policies over long time horizons. We pr...
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06/09/2016
Arbitrage-Free Combinatorial Market Making via Integer Programming
We present a new combinatorial market maker that operates arbitrage-free...
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05/16/2016
Off-policy evaluation for slate recommendation
This paper studies the evaluation of policies that recommend an ordered ...
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10/07/2015
Budget Constraints in Prediction Markets
We give a detailed characterization of optimal trades under budget const...
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06/15/2015
Convex Risk Minimization and Conditional Probability Estimation
This paper proves, in very general settings, that convex risk minimizati...
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03/10/2015
Doubly Robust Policy Evaluation and Optimization
We study sequential decision making in environments where rewards are on...
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02/20/2015
Contextual Semibandits via Supervised Learning Oracles
We study an online decision making problem where on each round a learner...
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07/30/2014
Market Making with Decreasing Utility for Information
We study information elicitation in cost-function-based combinatorial pr...
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10/16/2012
Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits
We present and prove properties of a new offline policy evaluator for an...
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05/09/2012
First-Order Mixed Integer Linear Programming
Mixed integer linear programming (MILP) is a powerful representation oft...
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10/19/2011
A Reliable Effective Terascale Linear Learning System
We present a system and a set of techniques for learning linear predicto...
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06/13/2011
Efficient Optimal Learning for Contextual Bandits
We address the problem of learning in an online setting where the learne...
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03/23/2011
