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05/26/2023
A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks
We study the phenomenon of in-context learning (ICL) exhibited by large ...
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03/17/2023
An Empirical Evaluation of Federated Contextual Bandit Algorithms
As the adoption of federated learning increases for learning from sensit...
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02/07/2023
Leveraging User-Triggered Supervision in Contextual Bandits
We study contextual bandit (CB) problems, where the user can sometimes r...
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06/21/2022
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL
We study reward-free reinforcement learning (RL) under general non-linea...
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05/29/2022
Provable Benefits of Representational Transfer in Reinforcement Learning
We study the problem of representational transfer in RL, where an agent ...
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02/05/2022
Adversarially Trained Actor Critic for Offline Reinforcement Learning
We propose Adversarially Trained Actor Critic (ATAC), a new model-free a...
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01/31/2022
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach
We present BRIEE (Block-structured Representation learning with Interlea...
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10/17/2021
Provable RL with Exogenous Distractors via Multistep Inverse Dynamics
Many real-world applications of reinforcement learning (RL) require the ...
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06/13/2021
Bellman-consistent Pessimism for Offline Reinforcement Learning
The use of pessimism, when reasoning about datasets lacking exhaustive e...
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03/24/2021
Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation
Policy optimization methods are popular reinforcement learning algorithm...
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03/22/2021
Provably Correct Optimization and Exploration with Non-linear Policies
Policy optimization methods remain a powerful workhorse in empirical Rei...
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03/19/2021
Towards a Dimension-Free Understanding of Adaptive Linear Control
We study the problem of adaptive control of the linear quadratic regulat...
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02/14/2021
Model-free Representation Learning and Exploration in Low-rank MDPs
The low rank MDP has emerged as an important model for studying represen...
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07/16/2020
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning
Direct policy gradient methods for reinforcement learning are a successf...
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07/16/2020
Provably Good Batch Reinforcement Learning Without Great Exploration
Batch reinforcement learning (RL) is important to apply RL algorithms to...
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07/01/2020
Policy Improvement from Multiple Experts
Despite its promise, reinforcement learning's real-world adoption has be...
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06/19/2020
Optimizing Interactive Systems via Data-Driven Objectives
Effective optimization is essential for real-world interactive systems t...
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06/18/2020
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
In order to deal with the curse of dimensionality in reinforcement learn...
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06/18/2020
Reparameterized Variational Divergence Minimization for Stable Imitation
While recent state-of-the-art results for adversarial imitation-learning...
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03/28/2020
Federated Residual Learning
We study a new form of federated learning where the clients train person...
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03/04/2020
Taking a hint: How to leverage loss predictors in contextual bandits?
We initiate the study of learning in contextual bandits with the help of...
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08/01/2019
Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes
Policy gradient methods are among the most effective methods in challeng...
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06/23/2019
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting
A learned generative model often produces biased statistics relative to ...
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06/10/2019
On the Optimality of Sparse Model-Based Planning for Markov Decision Processes
This work considers the sample complexity of obtaining an ϵ-optimal poli...
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06/09/2019
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
We design a new algorithm for batch active learning with deep neural net...
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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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05/12/2019
Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations
Assemblies of modular subsystems are being pressed into service to perfo...
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04/17/2019
Off-Policy Policy Gradient with State Distribution Correction
We study the problem of off-policy policy optimization in Markov decisio...
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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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01/02/2019
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
We investigate the feasibility of learning from both fully-labeled super...
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11/21/2018
Model-Based Reinforcement Learning in Contextual Decision Processes
We study the sample complexity of model-based reinforcement learning in ...
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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
On Polynomial Time PAC Reinforcement Learning with Rich Observations
We study the computational tractability of provably sample-efficient (PA...
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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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02/12/2018
Practical Evaluation and Optimization of Contextual Bandit Algorithms
We study and empirically optimize contextual bandit learning, exploratio...
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08/05/2017
Efficient Contextual Bandits in Non-stationary Worlds
Most contextual bandit algorithms minimize regret to the best fixed poli...
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03/03/2017
Active Learning for Cost-Sensitive Classification
We design an active learning algorithm for cost-sensitive multiclass cla...
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12/19/2016
Corralling a Band of Bandit Algorithms
We study the problem of combining multiple bandit algorithms (that is, o...
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10/29/2016
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable
This paper studies systematic exploration for reinforcement learning wit...
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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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03/14/2016
Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains
High-dimensional observations and complex real-world dynamics present ma...
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02/08/2016
PAC Reinforcement Learning with Rich Observations
We propose and study a new model for reinforcement learning with rich ob...
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06/29/2015
Efficient and Parsimonious Agnostic Active Learning
We develop a new active learning algorithm for the streaming setting sat...
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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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02/08/2015
Learning to Search Better Than Your Teacher
Methods for learning to search for structured prediction typically imita...
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10/02/2014
A Lower Bound for the Optimization of Finite Sums
This paper presents a lower bound for optimizing a finite sum of n funct...
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10/02/2014
Scalable Nonlinear Learning with Adaptive Polynomial Expansions
Can we effectively learn a nonlinear representation in time comparable t...
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02/04/2014
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
We present a new algorithm for the contextual bandit learning problem, w...
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10/07/2013
