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06/22/2023
Context-lumpable stochastic bandits
We consider a contextual bandit problem with S contexts and A actions. I...
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05/17/2023
Sequential Best-Arm Identification with Application to Brain-Computer Interface
A brain-computer interface (BCI) is a technology that enables direct com...
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02/10/2023
A Second-Order Method for Stochastic Bandit Convex Optimisation
We introduce a simple and efficient algorithm for unconstrained zeroth-o...
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02/07/2023
Linear Partial Monitoring for Sequential Decision-Making: Algorithms, Regret Bounds and Applications
Partial monitoring is an expressive framework for sequential decision-ma...
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02/07/2023
Leveraging Demonstrations to Improve Online Learning: Quality Matters
We investigate the extent to which offline demonstration data can improv...
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06/09/2022
Regret Bounds for Information-Directed Reinforcement Learning
Information-directed sampling (IDS) has revealed its potential as a data...
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05/26/2022
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost
We study distributed contextual linear bandits with stochastic contexts,...
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05/22/2022
Contextual Information-Directed Sampling
Information-directed sampling (IDS) has recently demonstrated its potent...
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02/22/2022
Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini's Regret
We show that a version of the generalised information ratio of Lattimore...
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10/29/2021
Variational Bayesian Optimistic Sampling
We consider online sequential decision problems where an agent must bala...
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06/03/2021
Bandit Phase Retrieval
We study a bandit version of phase retrieval where the learner chooses a...
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06/01/2021
Minimax Regret for Bandit Convex Optimisation of Ridge Functions
We analyse adversarial bandit convex optimisation with an adversary that...
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05/29/2021
Information Directed Sampling for Sparse Linear Bandits
Stochastic sparse linear bandits offer a practical model for high-dimens...
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04/06/2021
On the Optimality of Batch Policy Optimization Algorithms
Batch policy optimization considers leveraging existing data for policy ...
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01/06/2021
Geometric Entropic Exploration
Exploration is essential for solving complex Reinforcement Learning (RL)...
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11/11/2020
Asymptotically Optimal Information-Directed Sampling
We introduce a computationally efficient algorithm for finite stochastic...
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11/08/2020
High-Dimensional Sparse Linear Bandits
Stochastic linear bandits with high-dimensional sparse features are a pr...
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11/08/2020
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient
This paper provides a statistical analysis of high-dimensional batch Rei...
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11/08/2020
Online Sparse Reinforcement Learning
We investigate the hardness of online reinforcement learning in fixed ho...
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09/25/2020
Mirror Descent and the Information Ratio
We establish a connection between the stability of mirror descent and th...
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06/10/2020
Gaussian Gated Linear Networks
We propose the Gaussian Gated Linear Network (G-GLN), an extension to th...
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06/09/2020
Stochastic matrix games with bandit feedback
We study a version of the classical zero-sum matrix game with unknown pa...
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05/31/2020
Improved Regret for Zeroth-Order Adversarial Bandit Convex Optimisation
We prove that the information-theoretic upper bound on the minimax regre...
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03/03/2020
Model Selection in Contextual Stochastic Bandit Problems
We study model selection in stochastic bandit problems. Our approach rel...
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02/25/2020
Information Directed Sampling for Linear Partial Monitoring
Partial monitoring is a rich framework for sequential decision making un...
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11/18/2019
Learning with Good Feature Representations in Bandits and in RL with a Generative Model
The construction in the recent paper by Du et al. [2019] implies that se...
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10/15/2019
Adaptive Exploration in Linear Contextual Bandit
Contextual bandits serve as a fundamental model for many sequential deci...
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09/30/2019
Gated Linear Networks
This paper presents a family of backpropagation-free neural architecture...
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08/09/2019
Behaviour Suite for Reinforcement Learning
This paper introduces the Behaviour Suite for Reinforcement Learning, or...
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07/30/2019
Iterative Budgeted Exponential Search
We tackle two long-standing problems related to re-expansions in heurist...
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07/12/2019
Exploration by Optimisation in Partial Monitoring
We provide a simple and efficient algorithm for adversarial k-action d-o...
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06/07/2019
Zooming Cautiously: Linear-Memory Heuristic Search With Node Expansion Guarantees
We introduce and analyze two parameter-free linear-memory tree search al...
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05/28/2019
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio
The information-theoretic analysis by Russo and Van Roy (2014) in combin...
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03/19/2019
Adaptivity, Variance and Separation for Adversarial Bandits
We make three contributions to the theory of k-armed adversarial bandits...
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02/27/2019
Degenerate Feedback Loops in Recommender Systems
Machine learning is used extensively in recommender systems deployed in ...
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02/01/2019
An Information-Theoretic Approach to Minimax Regret in Partial Monitoring
We prove a new minimax theorem connecting the worst-case Bayesian regret...
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01/31/2019
A Geometric Perspective on Optimal Representations for Reinforcement Learning
This paper proposes a new approach to representation learning based on g...
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01/08/2019
Soft-Bayes: Prod for Mixtures of Experts with Log-Loss
We consider prediction with expert advice under the log-loss with the go...
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11/27/2018
Single-Agent Policy Tree Search With Guarantees
We introduce two novel tree search algorithms that use a policy to guide...
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11/13/2018
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits
We propose a multi-armed bandit algorithm that explores based on randomi...
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10/05/2018
Online Learning to Rank with Features
We introduce a new model for online ranking in which the click probabili...
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06/15/2018
BubbleRank: Safe Online Learning to Rerank
We study the problem of online learning to re-rank, where users provide ...
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06/06/2018
TopRank: A practical algorithm for online stochastic ranking
Online learning to rank is a sequential decision-making problem where in...
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05/23/2018
Cleaning up the neighborhood: A full classification for adversarial partial monitoring
Partial monitoring is a generalization of the well-known multi-armed ban...
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12/05/2017
Online Learning with Gated Linear Networks
This paper describes a family of probabilistic architectures designed fo...
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03/27/2017
A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis
Existing strategies for finite-armed stochastic bandits mostly depend on...
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03/22/2017
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning
Statistical performance bounds for reinforcement learning (RL) algorithm...
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10/14/2016
The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits
Stochastic linear bandits are a natural and simple generalisation of fin...
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06/10/2016
Causal Bandits: Learning Good Interventions via Causal Inference
We study the problem of using causal models to improve the rate at which...
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05/24/2016
