
Bandit Phase Retrieval
We study a bandit version of phase retrieval where the learner chooses a...
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Minimax Regret for Bandit Convex Optimisation of Ridge Functions
We analyse adversarial bandit convex optimisation with an adversary that...
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Information Directed Sampling for Sparse Linear Bandits
Stochastic sparse linear bandits offer a practical model for highdimens...
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On the Optimality of Batch Policy Optimization Algorithms
Batch policy optimization considers leveraging existing data for policy ...
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Geometric Entropic Exploration
Exploration is essential for solving complex Reinforcement Learning (RL)...
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Asymptotically Optimal InformationDirected Sampling
We introduce a computationally efficient algorithm for finite stochastic...
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HighDimensional Sparse Linear Bandits
Stochastic linear bandits with highdimensional sparse features are a pr...
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Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient
This paper provides a statistical analysis of highdimensional batch Rei...
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Online Sparse Reinforcement Learning
We investigate the hardness of online reinforcement learning in fixed ho...
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Mirror Descent and the Information Ratio
We establish a connection between the stability of mirror descent and th...
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Gaussian Gated Linear Networks
We propose the Gaussian Gated Linear Network (GGLN), an extension to th...
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Stochastic matrix games with bandit feedback
We study a version of the classical zerosum matrix game with unknown pa...
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Improved Regret for ZerothOrder Adversarial Bandit Convex Optimisation
We prove that the informationtheoretic upper bound on the minimax regre...
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Model Selection in Contextual Stochastic Bandit Problems
We study model selection in stochastic bandit problems. Our approach rel...
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Information Directed Sampling for Linear Partial Monitoring
Partial monitoring is a rich framework for sequential decision making un...
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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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Adaptive Exploration in Linear Contextual Bandit
Contextual bandits serve as a fundamental model for many sequential deci...
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Gated Linear Networks
This paper presents a family of backpropagationfree neural architecture...
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Behaviour Suite for Reinforcement Learning
This paper introduces the Behaviour Suite for Reinforcement Learning, or...
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Iterative Budgeted Exponential Search
We tackle two longstanding problems related to reexpansions in heurist...
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Exploration by Optimisation in Partial Monitoring
We provide a simple and efficient algorithm for adversarial kaction do...
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Zooming Cautiously: LinearMemory Heuristic Search With Node Expansion Guarantees
We introduce and analyze two parameterfree linearmemory tree search al...
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Connections Between Mirror Descent, Thompson Sampling and the Information Ratio
The informationtheoretic analysis by Russo and Van Roy (2014) in combin...
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Adaptivity, Variance and Separation for Adversarial Bandits
We make three contributions to the theory of karmed adversarial bandits...
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Degenerate Feedback Loops in Recommender Systems
Machine learning is used extensively in recommender systems deployed in ...
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An InformationTheoretic Approach to Minimax Regret in Partial Monitoring
We prove a new minimax theorem connecting the worstcase Bayesian regret...
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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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SoftBayes: Prod for Mixtures of Experts with LogLoss
We consider prediction with expert advice under the logloss with the go...
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SingleAgent Policy Tree Search With Guarantees
We introduce two novel tree search algorithms that use a policy to guide...
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Garbage In, Reward Out: Bootstrapping Exploration in MultiArmed Bandits
We propose a multiarmed bandit algorithm that explores based on randomi...
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Online Learning to Rank with Features
We introduce a new model for online ranking in which the click probabili...
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BubbleRank: Safe Online Learning to Rerank
We study the problem of online learning to rerank, where users provide ...
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TopRank: A practical algorithm for online stochastic ranking
Online learning to rank is a sequential decisionmaking problem where in...
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Cleaning up the neighborhood: A full classification for adversarial partial monitoring
Partial monitoring is a generalization of the wellknown multiarmed ban...
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Online Learning with Gated Linear Networks
This paper describes a family of probabilistic architectures designed fo...
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A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis
Existing strategies for finitearmed stochastic bandits mostly depend on...
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Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning
Statistical performance bounds for reinforcement learning (RL) algorithm...
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The End of Optimism? An Asymptotic Analysis of FiniteArmed Linear Bandits
Stochastic linear bandits are a natural and simple generalisation of fin...
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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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Refined Lower Bounds for Adversarial Bandits
We provide new lower bounds on the regret that must be suffered by adver...
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Regret Analysis of the Anytime Optimally Confident UCB Algorithm
I introduce and analyse an anytime version of the Optimally Confident UC...
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Thompson Sampling is Asymptotically Optimal in General Environments
We discuss a variant of Thompson sampling for nonparametric reinforcemen...
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Conservative Bandits
We study a novel multiarmed bandit problem that models the challenge fa...
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Regret Analysis of the FiniteHorizon Gittins Index Strategy for MultiArmed Bandits
I analyse the frequentist regret of the famous Gittins index strategy fo...
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Concentration and Confidence for Discrete Bayesian Sequence Predictors
Bayesian sequence prediction is a simple technique for predicting future...
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Asymptotically Optimal Agents
Artificial general intelligence aims to create agents capable of learnin...
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Time Consistent Discounting
A possibly immortal agent tries to maximise its summed discounted reward...
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