research
∙
08/15/2023
Unbiased Decisions Reduce Regret: Adversarial Domain Adaptation for the Bank Loan Problem
In many real world settings binary classification decisions are made bas...
share
research
∙
07/24/2023
Anytime Model Selection in Linear Bandits
Model selection in the context of bandit optimization is a challenging p...
research
∙
06/26/2023
Supervised Pretraining Can Learn In-Context Reinforcement Learning
Large transformer models trained on diverse datasets have shown a remark...
research
∙
06/09/2023
A Unified Model and Dimension for Interactive Estimation
We study an abstract framework for interactive learning called interacti...
research
∙
06/05/2023
Data-Driven Regret Balancing for Online Model Selection in Bandits
We consider model selection for sequential decision making in stochastic...
research
∙
06/01/2023
Improving Offline RL by Blending Heuristics
We propose Heuristic Blending (HUBL), a simple performance-improving tec...
research
∙
02/19/2023
Estimating Optimal Policy Value in General Linear Contextual Bandits
In many bandit problems, the maximal reward achievable by a policy is of...
research
∙
11/26/2022
Transfer RL via the Undo Maps Formalism
Transferring knowledge across domains is one of the most fundamental pro...
research
∙
11/09/2022
Leveraging Offline Data in Online Reinforcement Learning
Two central paradigms have emerged in the reinforcement learning (RL) co...
research
∙
10/23/2022
Learning General World Models in a Handful of Reward-Free Deployments
Building generally capable agents is a grand challenge for deep reinforc...
research
∙
10/18/2022
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity
Reinforcement learning provides an automated framework for learning beha...
research
∙
07/26/2022
Neural Design for Genetic Perturbation Experiments
The problem of how to genetically modify cells in order to maximize a ce...
research
∙
06/29/2022
Best of Both Worlds Model Selection
We study the problem of model selection in bandit scenarios in the prese...
research
∙
06/24/2022
Joint Representation Training in Sequential Tasks with Shared Structure
Classical theory in reinforcement learning (RL) predominantly focuses on...
research
∙
05/15/2022
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback
Motivated by applications to online learning in sparse estimation and Ba...
research
∙
01/21/2022
Meta Learning MDPs with Linear Transition Models
We study meta-learning in Markov Decision Processes (MDP) with linear tr...
research
∙
12/03/2021
Neural Pseudo-Label Optimism for the Bank Loan Problem
We study a class of classification problems best exemplified by the bank...
research
∙
11/08/2021
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit
We study the problem of information sharing and cooperation in Multi-Pla...
research
∙
11/04/2021
Towards an Understanding of Default Policies in Multitask Policy Optimization
Much of the recent success of deep reinforcement learning has been drive...
research
∙
10/27/2021
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection
We study the role of the representation of state-action value functions ...
research
∙
06/15/2021
Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity
Reinforcement learning (RL) is empirically successful in complex nonline...
research
∙
05/29/2021
On the Theory of Reinforcement Learning with Once-per-Episode Feedback
We study a theory of reinforcement learning (RL) in which the learner re...
research
∙
05/21/2021
Parallelizing Contextual Linear Bandits
Standard approaches to decision-making under uncertainty focus on sequen...
research
∙
03/17/2021
Near Optimal Policy Optimization via REPS
Since its introduction a decade ago, relative entropy policy search (REP...
research
∙
02/08/2021
Unlocking Pixels for Reinforcement Learning via Implicit Attention
There has recently been significant interest in training reinforcement l...
research
∙
02/07/2021
Deep Reinforcement Learning with Dynamic Optimism
In recent years, deep off-policy actor-critic algorithms have become a d...
research
∙
01/19/2021
ES-ENAS: Combining Evolution Strategies with Neural Architecture Search at No Extra Cost for Reinforcement Learning
We introduce ES-ENAS, a simple neural architecture search (NAS) algorith...
research
∙
01/06/2021
Fairness with Continuous Optimal Transport
Whilst optimal transport (OT) is increasingly being recognized as a powe...
research
∙
12/24/2020
Regret Bound Balancing and Elimination for Model Selection in Bandits and RL
We propose a simple model selection approach for algorithms in stochasti...
research
∙
11/19/2020
Online Model Selection for Reinforcement Learning with Function Approximation
Deep reinforcement learning has achieved impressive successes yet often ...
research
∙
11/12/2020
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian
Over the last decade, a single algorithm has changed many facets of our ...
research
∙
07/01/2020
Accelerated Message Passing for Entropy-Regularized MAP Inference
Maximum a posteriori (MAP) inference in discrete-valued Markov random fi...
research
∙
06/21/2020
On Optimism in Model-Based Reinforcement Learning
The principle of optimism in the face of uncertainty is prevalent throug...
research
∙
06/17/2020
Stochastic Bandits with Linear Constraints
We study a constrained contextual linear bandit setting, where the goal ...
research
∙
06/09/2020
Regret Balancing for Bandit and RL Model Selection
We consider model selection in stochastic bandit and reinforcement learn...
research
∙
06/08/2020
Learning the Truth From Only One Side of the Story
Learning under one-sided feedback (i.e., where examples arrive in an onl...
research
∙
03/30/2020
Stochastic Flows and Geometric Optimization on the Orthogonal Group
We present a new class of stochastic, geometrically-driven optimization ...
research
∙
03/05/2020
Robustness Guarantees for Mode Estimation with an Application to Bandits
Mode estimation is a classical problem in statistics with a wide range o...
research
∙
03/03/2020
Model Selection in Contextual Stochastic Bandit Problems
We study model selection in stochastic bandit problems. Our approach rel...
research
∙
02/23/2020
On Thompson Sampling with Langevin Algorithms
Thompson sampling is a methodology for multi-armed bandit problems that ...
research
∙
02/07/2020
Ready Policy One: World Building Through Active Learning
Model-Based Reinforcement Learning (MBRL) offers a promising direction f...
research
∙
02/03/2020
Effective Diversity in Population-Based Reinforcement Learning
Maintaining a population of solutions has been shown to increase explora...
research
∙
09/25/2019
ES-MAML: Simple Hessian-Free Meta Learning
We introduce ES-MAML, a new framework for solving the model agnostic met...
research
∙
07/28/2019
Wasserstein Fair Classification
We propose an approach to fair classification that enforces independence...
research
∙
07/10/2019
Reinforcement Learning with Chromatic Networks
We present a new algorithm for finding compact neural networks encoding ...
research
∙
07/02/2019
Approximate Sherali-Adams Relaxations for MAP Inference via Entropy Regularization
Maximum a posteriori (MAP) inference is a fundamental computational para...
research
∙
06/11/2019
Wasserstein Reinforcement Learning
We propose behavior-driven optimization via Wasserstein distances (WDs) ...
research
∙
05/29/2019
Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes
We propose a new class of structured methods for Monte Carlo (MC) sampli...
research
∙
03/07/2019
Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces
We present a new algorithm ASEBO for conducting optimization of high-dim...
research
∙
03/07/2019