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Computer Science

D-Index
31
Citations
3324
World Ranking
13762
National Ranking
5465

Overview

Branislav Kveton is affiliated with Adobe Systems in the United States. Their research spans several subfields of computer science, with a particular focus on artificial intelligence and decision sciences.

The main fields of study in which Branislav Kveton has contributed include:

  • Computer Science
  • Decision Sciences

Their work extends across a number of subfields, notably:

  • Artificial Intelligence
  • Management Science and Operations Research
  • Computational Theory and Mathematics
  • Information Systems
  • Computer Science Applications

Kveton has focused research topics in areas such as:

  • Advanced Bandit Algorithms Research
  • Machine Learning and Algorithms
  • Reinforcement Learning in Robotics
  • Data Stream Mining Techniques
  • Advanced Multi-Objective Optimization Algorithms
  • Recommender Systems and Techniques
  • Machine Learning and Data Classification

Recent papers authored or coauthored by Branislav Kveton include:

  • "Kernel-Based Reinforcement Learning on Representative States," 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Latent Bandits Revisited," 2020, arXiv (Cornell University)
  • "Personalization of Large Language Models: A Survey," 2024, arXiv (Cornell University)
  • "Towards Simulation-Based Evaluation of Recommender Systems with Carousel Interfaces," 2024, ACM Transactions on Recommender Systems
  • "Differentiable Bandit Exploration," 2020, arXiv (Cornell University)

Kveton frequently collaborates with other researchers, including:

  • Manzil Zaheer
  • Mohammad Ghavamzadeh
  • Sumeet Katariya
  • Craig Boutilier
  • Joey Hong

The scientist has published extensively in various venues, with a concentration in:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
  • ACM Transactions on Recommender Systems
  • Computer Communications

Best Publications

  • {Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits}

    Branislav Kveton;Zheng Wen;Azin Ashkan;Csaba Szepesvari

  • Cascading Bandits: Learning to Rank in the Cascade Model

    Branislav Kveton;Csaba Szepesvari;Zheng Wen;Azin Ashkan

  • Efficient Thompson sampling for online matrix-factorization recommendation

    Jaya Kawale;Hung Bui;Branislav Kveton;Long Tran Thanh

  • User identification and personalized vehicle settings management system

    Victor B. Lortz;Branislav Kveton;Vijay Sarathi Kesavan;Somya Rathi

  • Matroid bandits: fast combinatorial optimization with learning

    Branislav Kveton;Zheng Wen;Azin Ashkan;Hoda Eydgahi

  • Spectral Bandits for Smooth Graph Functions

    Michal Valko;Remi Munos;Branislav Kveton;Tom Koc k

  • Solving factored MDPs with continuous and discrete variables

    Carlos Guestrin;Milos Hauskrecht;Branislav Kveton

  • When gossip is good: distributed probabilistic inference for detection of slow network intrusions

    Denver Dash;Branislav Kveton;John Mark Agosta;Eve Schooler

  • Optimal greedy diversity for recommendation

    Azin Ashkan;Branislav Kveton;Shlomo Berkovsky;Zheng Wen

  • Online influence maximization under independent cascade model with semi-bandit feedback

    Zheng Wen;Branislav Kveton;Michal Valko;Sharan Vaswani

  • Leveraging side observations in stochastic bandits

    Stéphane Caron;Branislav Kveton;Marc Lelarge;Smriti Bhagat

  • Combinatorial cascading bandits

    Branislav Kveton;Zheng Wen;Azin Ashkan;Csaba Szepesvári

  • Efficient Learning in Large-Scale Combinatorial Semi-Bandits

    Zheng Wen;Branislav Kveton;Azin Ashkan

  • How to hide the elephant- or the donkey- in the room: Practical privacy against statistical inference for large data

    Salman Salamatian;Amy Zhang;Flavio Du Pin Calmon;Sandilya Bhamidipati

  • Linear Program Approximations for Factored Continuous-State Markov Decision Processes

    Milos Hauskrecht;Branislav Kveton

  • Cascading bandits for large-scale recommendation problems

    Shi Zong;Hao Ni;Kenny Sung;Nan Rosemary Ke

  • Managing Your Private and Public Data: Bringing Down Inference Attacks Against Your Privacy

    Salman Salamatian;Amy Zhang;Flavio du Pin Calmon;Sandilya Bhamidipati

  • Solving factored MDPs with hybrid state and action variables

    Branislav Kveton;Milos Hauskrecht;Carlos Guestrin

  • DCM bandits: learning to rank with multiple clicks

    Sumeet Katariya;Branislav Kveton;Csaba Szepesvári;Zheng Wen

  • Online learning to rank in stochastic click models

    Masrour Zoghi;Tomas Tunys;Mohammad Ghavamzadeh;Branislav Kveton

  • Inferring Identity Using Accelerometers in Television Remote Controls

    Keng-Hao Chang;Jeffrey Hightower;Branislav Kveton

  • Bernoulli Rank-1 Bandits for Click Feedback

    Sumeet Katariya;Branislav Kveton;Csaba Szepesvári;Claire Vernade

  • Leveraging Side Observations in Stochastic Bandits

    Stephane Caron;Branislav Kveton;Marc Lelarge;Smriti Bhagat

  • Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits

    Branislav Kveton;Zheng Wen;Azin Ashkan;Csaba Szepesvari

  • Randomized Exploration in Generalized Linear Bandits

    Branislav Kveton;Manzil Zaheer;Csaba Szepesvári;Lihong Li

Frequent Co-Authors

Csaba Szepesvári
Csaba Szepesvári University of Alberta
Mohammad Ghavamzadeh
Mohammad Ghavamzadeh Amazon (United States)
Craig Boutilier
Craig Boutilier Google (United States)
Shlomo Berkovsky
Shlomo Berkovsky Macquarie University
Nina Taft
Nina Taft Google (United States)
Amr Ahmed
Amr Ahmed University of Nottingham Malaysia Campus
Carlos Guestrin
Carlos Guestrin Stanford University
Mark Schmidt
Mark Schmidt University of British Columbia
Maarten de Rijke
Maarten de Rijke University of Amsterdam
Christophe Diot
Christophe Diot Google (United States)

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