
Logtime Prediction Markets for Interval Securities
We design a prediction market to recover a complete and fully general pr...
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Gradient descent follows the regularization path for general losses
Recent work across many machine learning disciplines has highlighted tha...
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Constrained episodic reinforcement learning in concaveconvex and knapsack settings
We propose an algorithm for tabular episodic reinforcement learning with...
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Doubly robust offpolicy evaluation with shrinkage
We design a new family of estimators for offpolicy evaluation in contex...
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Reinforcement Learning with Convex Constraints
In standard reinforcement learning (RL), a learning agent seeks to optim...
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Fair Regression: Quantitative Definitions and Reductionbased Algorithms
In this paper, we study the prediction of a realvalued target, such as ...
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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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A Reductions Approach to Fair Classification
We present a systematic approach for achieving fairness in a binary clas...
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Practical Contextual Bandits with Regression Oracles
A major challenge in contextual bandits is to design generalpurpose alg...
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Hierarchical Imitation and Reinforcement Learning
We study the problem of learning policies over long time horizons. We pr...
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ArbitrageFree Combinatorial Market Making via Integer Programming
We present a new combinatorial market maker that operates arbitragefree...
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Offpolicy evaluation for slate recommendation
This paper studies the evaluation of policies that recommend an ordered ...
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Budget Constraints in Prediction Markets
We give a detailed characterization of optimal trades under budget const...
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Convex Risk Minimization and Conditional Probability Estimation
This paper proves, in very general settings, that convex risk minimizati...
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Doubly Robust Policy Evaluation and Optimization
We study sequential decision making in environments where rewards are on...
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Contextual Semibandits via Supervised Learning Oracles
We study an online decision making problem where on each round a learner...
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Market Making with Decreasing Utility for Information
We study information elicitation in costfunctionbased combinatorial pr...
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Sampleefficient Nonstationary Policy Evaluation for Contextual Bandits
We present and prove properties of a new offline policy evaluator for an...
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FirstOrder Mixed Integer Linear Programming
Mixed integer linear programming (MILP) is a powerful representation oft...
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A Reliable Effective Terascale Linear Learning System
We present a system and a set of techniques for learning linear predicto...
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Efficient Optimal Learning for Contextual Bandits
We address the problem of learning in an online setting where the learne...
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Doubly Robust Policy Evaluation and Learning
We study decision making in environments where the reward is only partia...
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