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05/31/2023
Online-to-PAC Conversions: Generalization Bounds via Regret Analysis
We present a new framework for deriving bounds on the generalization bou...
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05/22/2023
Offline Primal-Dual Reinforcement Learning for Linear MDPs
Offline Reinforcement Learning (RL) aims to learn a near-optimal policy ...
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05/01/2023
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits
We consider the adversarial linear contextual bandit setting, which allo...
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02/27/2023
Optimistic Planning by Regularized Dynamic Programming
We propose a new method for optimistic planning in infinite-horizon disc...
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10/21/2022
Efficient Global Planning in Large MDPs via Stochastic Primal-Dual Optimization
We propose a new stochastic primal-dual optimization algorithm for plann...
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10/17/2022
Sufficient Exploration for Convex Q-learning
In recent years there has been a collective research effort to find new ...
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09/22/2022
Proximal Point Imitation Learning
This work develops new algorithms with rigorous efficiency guarantees fo...
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07/18/2022
Online Learning with Off-Policy Feedback
We study the problem of online learning in adversarial bandit problems u...
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05/27/2022
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits
We study the Bayesian regret of the renowned Thompson Sampling algorithm...
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02/10/2022
Generalization Bounds via Convex Analysis
Since the celebrated works of Russo and Zou (2016,2019) and Xu and Ragin...
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12/28/2021
Robustness and risk management via distributional dynamic programming
In dynamic programming (DP) and reinforcement learning (RL), an agent le...
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09/24/2021
Learning to maximize global influence from local observations
We study a family online influence maximization problems where in a sequ...
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02/01/2021
Information-Theoretic Generalization Bounds for Stochastic Gradient Descent
We study the generalization properties of the popular stochastic gradien...
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10/21/2020
Logistic Q-Learning
We propose a new reinforcement learning algorithm derived from a regular...
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07/03/2020
A Unifying View of Optimism in Episodic Reinforcement Learning
The principle of optimism in the face of uncertainty underpins many theo...
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07/03/2020
Online learning in MDPs with linear function approximation and bandit feedback
We consider an online learning problem where the learner interacts with ...
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02/01/2020
Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits
We consider an adversarial variant of the classic K-armed linear context...
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01/28/2020
Fast Rates for Online Prediction with Abstention
In the setting of sequential prediction of individual {0, 1}-sequences w...
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09/22/2019
Faster saddle-point optimization for solving large-scale Markov decision processes
We consider the problem of computing optimal policies in average-reward ...
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06/19/2019
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
We consider the core reinforcement-learning problem of on-policy value f...
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02/22/2019
Beating SGD Saturation with Tail-Averaging and Minibatching
While stochastic gradient descent (SGD) is one of the major workhorses i...
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02/08/2019
Bandit Principal Component Analysis
We consider a partial-feedback variant of the well-studied online PCA pr...
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05/28/2018
Potential and Pitfalls of Multi-Armed Bandits for Decentralized Spatial Reuse in WLANs
Spatial Reuse (SR) has recently gained attention for performance maximiz...
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05/28/2018
Online Influence Maximization with Local Observations
We consider an online influence maximization problem in which a decision...
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02/22/2018
Iterate averaging as regularization for stochastic gradient descent
We propose and analyze a variant of the classic Polyak-Ruppert averaging...
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02/05/2018
Wireless Optimisation via Convex Bandits: Unlicensed LTE/WiFi Coexistence
Bandit Convex Optimisation (BCO) is a powerful framework for sequential ...
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10/31/2017
Collaborative Spatial Reuse in Wireless Networks via Selfish Multi-Armed Bandits
Next-generation wireless deployments are characterized by being dense an...
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10/16/2017
On the Hardness of Inventory Management with Censored Demand Data
We consider a repeated newsvendor problem where the inventory manager ha...
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05/29/2017
Boltzmann Exploration Done Right
Boltzmann exploration is a classic strategy for sequential decision-maki...
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05/22/2017
A unified view of entropy-regularized Markov decision processes
We propose a general framework for entropy-regularized average-reward re...
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02/28/2017
Algorithmic stability and hypothesis complexity
We introduce a notion of algorithmic stability of learning algorithms---...
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02/21/2017
Fast rates for online learning in Linearly Solvable Markov Decision Processes
We study the problem of online learning in a class of Markov decision pr...
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06/10/2015
Explore no more: Improved high-probability regret bounds for non-stochastic bandits
This work addresses the problem of regret minimization in non-stochastic...
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03/17/2015
Importance weighting without importance weights: An efficient algorithm for combinatorial semi-bandits
We propose a sample-efficient alternative for importance weighting for s...
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02/23/2015
First-order regret bounds for combinatorial semi-bandits
We consider the problem of online combinatorial optimization under semi-...
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06/26/2014
Online learning in MDPs with side information
We study online learning of finite Markov decision process (MDP) problem...
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06/20/2012
