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06/02/2023
A Convex Relaxation Approach to Bayesian Regret Minimization in Offline Bandits
Algorithms for offline bandits must optimize decisions in uncertain envi...
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05/25/2023
DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
Learning from human feedback has been shown to improve text-to-image mod...
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05/12/2023
Private and Communication-Efficient Algorithms for Entropy Estimation
Modern statistical estimation is often performed in a distributed settin...
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04/24/2023
On Dynamic Program Decompositions of Static Risk Measures
Optimizing static risk-averse objectives in Markov decision processes is...
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04/22/2023
A Review of Deep Learning for Video Captioning
Video captioning (VC) is a fast-moving, cross-disciplinary area of resea...
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02/23/2023
Aligning Text-to-Image Models using Human Feedback
Deep generative models have shown impressive results in text-to-image sy...
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02/21/2023
Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management
Reinforcement learning (RL) has shown great promise for developing dialo...
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12/09/2022
Multi-Task Off-Policy Learning from Bandit Feedback
Many practical applications, such as recommender systems and learning to...
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11/25/2022
Operator Splitting Value Iteration
We introduce new planning and reinforcement learning algorithms for disc...
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09/09/2022
RASR: Risk-Averse Soft-Robust MDPs with EVaR and Entropic Risk
Prior work on safe Reinforcement Learning (RL) has studied risk-aversion...
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08/10/2022
Robust Reinforcement Learning using Offline Data
The goal of robust reinforcement learning (RL) is to learn a policy that...
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07/01/2022
Reinforcement Learning of Multi-Domain Dialog Policies Via Action Embeddings
Learning task-oriented dialog policies via reinforcement learning typica...
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05/31/2022
A Mixture-of-Expert Approach to RL-based Dialogue Management
Despite recent advancements in language models (LMs), their application ...
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05/12/2022
Collaborative Multi-agent Stochastic Linear Bandits
We study a collaborative multi-agent stochastic linear bandit setting, w...
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05/12/2022
Multi-Environment Meta-Learning in Stochastic Linear Bandits
In this work we investigate meta-learning (or learning-to-learn) approac...
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05/10/2022
Efficient Risk-Averse Reinforcement Learning
In risk-averse reinforcement learning (RL), the goal is to optimize some...
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02/25/2022
Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms
We study a sequential decision problem where the learner faces a sequenc...
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02/25/2022
Meta-Learning for Simple Regret Minimization
We develop a meta-learning framework for simple regret minimization in b...
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02/03/2022
Deep Hierarchy in Bandits
Mean rewards of actions are often correlated. The form of these correlat...
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11/12/2021
Hierarchical Bayesian Bandits
Meta-, multi-task, and federated learning can be all viewed as solving s...
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06/10/2021
Thompson Sampling with a Mixture Prior
We study Thompson sampling (TS) in online decision-making problems where...
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06/09/2021
Parameter and Feature Selection in Stochastic Linear Bandits
We study two model selection settings in stochastic linear bandits (LB)....
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06/09/2021
Fixed-Budget Best-Arm Identification in Contextual Bandits: A Static-Adaptive Algorithm
We study the problem of best-arm identification (BAI) in contextual band...
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03/01/2021
Adaptive Sampling for Minimax Fair Classification
Machine learning models trained on imbalanced datasets can often end up ...
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12/01/2020
Non-Stationary Latent Bandits
Users of recommender systems often behave in a non-stationary fashion, d...
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11/30/2020
Soft-Robust Algorithms for Handling Model Misspecification
In reinforcement learning, robust policies for high-stakes decision-maki...
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11/12/2020
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Uncertainty quantification (UQ) plays a pivotal role in reduction of unc...
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09/14/2020
Variance-Reduced Off-Policy Memory-Efficient Policy Search
Off-policy policy optimization is a challenging problem in reinforcement...
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06/28/2020
Deep Bayesian Quadrature Policy Optimization
We study the problem of obtaining accurate policy gradient estimates. Th...
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06/24/2020
Control-Aware Representations for Model-based Reinforcement Learning
A major challenge in modern reinforcement learning (RL) is efficient con...
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06/17/2020
Stochastic Bandits with Linear Constraints
We study a constrained contextual linear bandit setting, where the goal ...
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06/09/2020
Variational Model-based Policy Optimization
Model-based reinforcement learning (RL) algorithms allow us to combine m...
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06/06/2020
Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity
In this paper, we introduce proximal gradient temporal difference learni...
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06/06/2020
Automatic Policy Synthesis to Improve the Safety of Nonlinear Dynamical Systems
Learning controllers merely based on a performance metric has been prove...
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05/20/2020
Mirror Descent Policy Optimization
We propose deep Reinforcement Learning (RL) algorithms inspired by mirro...
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03/06/2020
Active Model Estimation in Markov Decision Processes
We study the problem of efficient exploration in order to learn an accur...
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03/02/2020
Predictive Coding for Locally-Linear Control
High-dimensional observations and unknown dynamics are major challenges ...
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02/28/2020
Policy-Aware Model Learning for Policy Gradient Methods
This paper considers the problem of learning a model in model-based rein...
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02/08/2020
Improved Algorithms for Conservative Exploration in Bandits
In many fields such as digital marketing, healthcare, finance, and robot...
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02/08/2020
Conservative Exploration in Reinforcement Learning
While learning in an unknown Markov Decision Process (MDP), an agent sho...
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10/28/2019
Adaptive Sampling for Estimating Multiple Probability Distributions
We consider the problem of allocating samples to a finite set of discret...
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10/07/2019
Multi-step Greedy Policies in Model-Free Deep Reinforcement Learning
Multi-step greedy policies have been extensively used in model-based Rei...
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10/03/2019
Benchmarking Batch Deep Reinforcement Learning Algorithms
Widely-used deep reinforcement learning algorithms have been shown to fa...
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09/10/2019
Multi-Step Greedy and Approximate Real Time Dynamic Programming
Real Time Dynamic Programming (RTDP) is a well-known Dynamic Programming...
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09/04/2019
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control
Many real-world sequential decision-making problems can be formulated as...
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06/21/2019
Randomized Exploration in Generalized Linear Bandits
We study two randomized algorithms for generalized linear bandits, GLM-T...
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06/01/2019
Active Learning for Binary Classification with Abstention
We construct and analyze active learning algorithms for the problem of b...
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05/27/2019
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies
State-of-the-art efficient model-based Reinforcement Learning (RL) algor...
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05/23/2019
Binary Classification with Bounded Abstention Rate
We consider the problem of binary classification with abstention in the ...
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03/21/2019
