Branislav Kveton

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Senior Scientist at Adobe Research

Meta-Thompson Sampling

Efficient exploration in multi-armed bandits is a fundamental online lea...
02/11/2021 ∙ by Branislav Kveton, et al. ∙ 0 ∙ share

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Non-Stationary Latent Bandits

Users of recommender systems often behave in a non-stationary fashion, d...
12/01/2020 ∙ by Joey Hong, et al. ∙ 0 ∙ share

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Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems

We propose a novel framework for structured bandits, which we call an in...
07/09/2020 ∙ by Tong Yu, et al. ∙ 0 ∙ share

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Latent Bandits Revisited

A latent bandit problem is one in which the learning agent knows the arm...
06/15/2020 ∙ by Joey Hong, et al. ∙ 0 ∙ share

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Piecewise-Stationary Off-Policy Optimization

Off-policy learning is a framework for evaluating and optimizing policie...
06/15/2020 ∙ by Joey Hong, et al. ∙ 0 ∙ share

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Differentiable Meta-Learning in Contextual Bandits

We study a contextual bandit setting where the learning agent has access...
06/09/2020 ∙ by Branislav Kveton, et al. ∙ 0 ∙ share

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Sample Efficient Graph-Based Optimization with Noisy Observations

We study sample complexity of optimizing "hill-climbing friendly" functi...
06/04/2020 ∙ by Tan Nguyen, et al. ∙ 10 ∙ share

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Differentiable Bandit Exploration

We learn bandit policies that maximize the average reward over bandit in...
02/17/2020 ∙ by Craig Boutilier, et al. ∙ 22 ∙ share

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Old Dog Learns New Tricks: Randomized UCB for Bandit Problems

We propose RandUCB, a bandit strategy that uses theoretically derived co...
10/11/2019 ∙ by Sharan Vaswani, et al. ∙ 0 ∙ share

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Randomized Exploration in Generalized Linear Bandits

We study two randomized algorithms for generalized linear bandits, GLM-T...
06/21/2019 ∙ by Branislav Kveton, et al. ∙ 2 ∙ share

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Waterfall Bandits: Learning to Sell Ads Online

A popular approach to selling online advertising is by a waterfall, wher...
04/20/2019 ∙ by Branislav Kveton, et al. ∙ 6 ∙ share

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Empirical Bayes Regret Minimization

The prevalent approach to bandit algorithm design is to have a low-regre...
04/04/2019 ∙ by Chih-Wei Hsu, et al. ∙ 6 ∙ share

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Perturbed-History Exploration in Stochastic Linear Bandits

We propose a new online algorithm for minimizing the cumulative regret i...
03/21/2019 ∙ by Branislav Kveton, et al. ∙ 10 ∙ share

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Perturbed-History Exploration in Stochastic Multi-Armed Bandits

We propose an online algorithm for cumulative regret minimization in a s...
02/26/2019 ∙ by Branislav Kveton, et al. ∙ 4 ∙ share

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Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits

We propose a multi-armed bandit algorithm that explores based on randomi...
11/13/2018 ∙ by Branislav Kveton, et al. ∙ 6 ∙ share

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Online Diverse Learning to Rank from Partial-Click Feedback

Learning to rank is an important problem in machine learning and recomme...
11/01/2018 ∙ by Prakhar Gupta, et al. ∙ 8 ∙ share

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BubbleRank: Safe Online Learning to Rerank

We study the problem of online learning to re-rank, where users provide ...
06/15/2018 ∙ by Branislav Kveton, et al. ∙ 2 ∙ share

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TopRank: A practical algorithm for online stochastic ranking

Online learning to rank is a sequential decision-making problem where in...
06/06/2018 ∙ by Tor Lattimore, et al. ∙ 2 ∙ share

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Conservative Exploration using Interleaving

In many practical problems, a learning agent may want to learn the best ...
06/03/2018 ∙ by Sumeet Katariya, et al. ∙ 2 ∙ share

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New Insights into Bootstrapping for Bandits

We investigate the use of bootstrapping in the bandit setting. We first ...
05/24/2018 ∙ by Sharan Vaswani, et al. ∙ 0 ∙ share

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Offline Evaluation of Ranking Policies with Click Models

Many web systems rank and present a list of items to users, from recomme...
04/27/2018 ∙ by Shuai Li, et al. ∙ 0 ∙ share

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Nearly Optimal Adaptive Procedure for Piecewise-Stationary Bandit: a Change-Point Detection Approach

Multi-armed bandit (MAB) is a class of online learning problems where a ...
02/11/2018 ∙ by Yang Cao, et al. ∙ 0 ∙ share

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Stochastic Low-Rank Bandits

Many problems in computer vision and recommender systems involve low-ran...
12/13/2017 ∙ by Branislav Kveton, et al. ∙ 0 ∙ share

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SpectralFPL: Online Spectral Learning for Single Topic Models

This paper studies how to efficiently learn an optimal latent variable m...
09/21/2017 ∙ by Tong Yu, et al. ∙ 0 ∙ share

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Bernoulli Rank-1 Bandits for Click Feedback

The probability that a user will click a search result depends both on i...
03/19/2017 ∙ by Sumeet Katariya, et al. ∙ 0 ∙ share

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Online Learning to Rank in Stochastic Click Models

Online learning to rank is a core problem in information retrieval and m...
03/07/2017 ∙ by Masrour Zoghi, et al. ∙ 0 ∙ share

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Stochastic Rank-1 Bandits

We propose stochastic rank-1 bandits, a class of online learning problem...
08/10/2016 ∙ by Sumeet Katariya, et al. ∙ 0 ∙ share

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Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback

We study the stochastic online problem of learning to influence in a soc...
05/21/2016 ∙ by Zheng Wen, et al. ∙ 0 ∙ share

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Cascading Bandits for Large-Scale Recommendation Problems

Most recommender systems recommend a list of items. The user examines th...
03/17/2016 ∙ by Shi Zong, et al. ∙ 0 ∙ share

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DCM Bandits: Learning to Rank with Multiple Clicks

A search engine recommends to the user a list of web pages. The user exa...
02/09/2016 ∙ by Sumeet Katariya, et al. ∙ 0 ∙ share

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Graphical Model Sketch

Structured high-cardinality data arises in many domains, and poses a maj...
02/09/2016 ∙ by Branislav Kveton, et al. ∙ 0 ∙ share

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Cascading Bandits: Learning to Rank in the Cascade Model

A search engine usually outputs a list of K web pages. The user examines...
02/10/2015 ∙ by Branislav Kveton, et al. ∙ 0 ∙ share

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DUM: Diversity-Weighted Utility Maximization for Recommendations

The need for diversification of recommendation lists manifests in a numb...
11/13/2014 ∙ by Azin Ashkan, et al. ∙ 0 ∙ share

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Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits

A stochastic combinatorial semi-bandit is an online learning problem whe...
10/03/2014 ∙ by Branislav Kveton, et al. ∙ 0 ∙ share

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Efficient Learning in Large-Scale Combinatorial Semi-Bandits

A stochastic combinatorial semi-bandit is an online learning problem whe...
06/28/2014 ∙ by Zheng Wen, et al. ∙ 0 ∙ share

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Learning to Act Greedily: Polymatroid Semi-Bandits

Many important optimization problems, such as the minimum spanning tree ...
05/30/2014 ∙ by Branislav Kveton, et al. ∙ 0 ∙ share

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Matroid Bandits: Fast Combinatorial Optimization with Learning

A matroid is a notion of independence in combinatorial optimization whic...
03/20/2014 ∙ by Branislav Kveton, et al. ∙ 0 ∙ share

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Leveraging Side Observations in Stochastic Bandits

This paper considers stochastic bandits with side observations, a model ...
10/16/2012 ∙ by Stephane Caron, et al. ∙ 0 ∙ share

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Solving Factored MDPs with Continuous and Discrete Variables

Although many real-world stochastic planning problems are more naturally...
07/11/2012 ∙ by Carlos E. Guestrin, et al. ∙ 0 ∙ share

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Partitioned Linear Programming Approximations for MDPs

Approximate linear programming (ALP) is an efficient approach to solving...
06/13/2012 ∙ by Branislav Kveton, et al. ∙ 0 ∙ share

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Automatic Tuning of Interactive Perception Applications

Interactive applications incorporating high-data rate sensing and comput...
03/15/2012 ∙ by Qian Zhu, et al. ∙ 0 ∙ share

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Online Semi-Supervised Learning on Quantized Graphs

In this paper, we tackle the problem of online semi-supervised learning ...
03/15/2012 ∙ by Michal Valko, et al. ∙ 0 ∙ share

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Branislav Kveton

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