Branislav Kveton

Senior Research Scientist

BigData Experience Lab, San Jose

Branislav Kveton is a machine learning scientist at Adobe Research in San Jose. He was at Technicolor’s Research Center from 2011 to 2014, and at Intel Research from 2006 to 2011. Before 2006, he was a graduate student in the Intelligent Systems Program at the University of Pittsburgh. His advisor was Milos Hauskrecht.

He proposes, analyzes, and applies algorithms that learn incrementally, run in real time, and converge to near optimal solutions as the number of training examples increases. Most of his recent work is focused on online learning of structured problems, such as graphs, submodularity, matroids, polymatroids, and reinforcement learning.

Practical problems are often so massive that even low-order polynomial-time solutions are not practical. Fortunately, many optimization problems can be solved greedily, either optimally or suboptimally with guarantees. Two popular examples of such problems are finding the maximum of a modular function on a matroid and finding the maximum of a submodular function subject to a cardinality constraint. Recently, he proposed several algorithms for solving this kind of problems when the model of the problem is initially unknown / imperfect, and is learned by interacting repeatedly with the environment. These algorithms can solve many interesting real-world problems, such as learning near-optimal preference elicitation policies from eliciting preferences, and learning optimal policies for network routing from repeated rerouting.

My Publications

Bernoulli Rank-1 Bandits for Click Feedback

Katariya, S., Kveton, B., Szepesvari, C., Vernade, C., Wen, Z. (Aug. 19, 2017)
Proceedings of 26th International Joint Conference on Artificial Intelligence (IJCAI)

Model-Independent Online Learning for Influence Maximization

Vaswani, S., Kveton, B., Wen, Z., Ghavamzadeh, M., Lakshmanan, L., Schmidt, M. (Aug. 6, 2017)
Proceedings of International Conference on Machine Learning (ICML) 2017

Online Learning to Rank in Stochastic Click Models

Zoghi, M., Tunys, T., Ghavamzadeh, M., Kveton, B., Szepesvari, C., Wen, Z. (Aug. 6, 2017)
Proceedings of International Conference on Machine Learning (ICML) 2017

Get to the Bottom: Causal Analysis for User Modeling

Zong, S., Kveton, B., Berkovsky, S., Ashkan, A., Wen, Z. (Jul. 9, 2017)
Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (UMAP)

Stochastic Rank-1 Bandits

Katariya, S., Kveton, B., Szepesvari, C., Vernade, C., Wen, Z. (Apr. 20, 2017)
20th International Conference on Artificial Intelligence and Statistics

Does Weather Matter? Causal Analysis of TV Logs

Zong, S., Kveton, B., Berkovsky, S., Ashkan, A., Vlassis, N., Wen, Z. (Apr. 3, 2017)
26th International World Wide Web Conference

Graphical Model Sketch

Kveton, B., Bui, H., Ghavamzadeh, M., Theocharous, G., Muthukrishnan, S., Sun, S. (Sep. 19, 2016)
European Conference on Machine Learning and Knowledge Discovery in Databases

Minimal Interaction Content Discovery in Recommender Systems

Kveton, B., Berkovsky, S. (Jul. 31, 2016)
ACM Transactions on Interactive Intelligent Systems 6

Practical Linear Models for Large-Scale One-Class Collaborative Filtering

Sedhain, S., Bui, H., Kawale, J., Vlassis, N., Kveton, B., Menon, A., Bui, T., Sanner, S. (Jul. 9, 2016)
25th International Joint Conference on Artificial Intelligence

Cascading Bandits for Large-Scale Recommendation Problems

Zong, S., Ni, H., Sung, K., Ke, N., Wen, Z., Kveton, B. (Jun. 25, 2016)
32nd Conference on Uncertainty in Artificial Intelligence

DCM Bandits: Learning to Rank with Multiple Clicks

Katariya, S., Kveton, B., Szepesvari, C., Wen, Z. (Jun. 19, 2016)
33rd International Conference on Machine Learning

Combinatorial Cascading Bandits

Kveton, B., Wen, Z., Ashkan, A., Szepesvari, C. (Dec. 7, 2015)
Advances in Neural Information Processing Systems 28

Efficient Thompson Sampling for Online Matrix-Factorization Recommendation

Kawale, J., Bui, H., Kveton, B., Tran-Thanh, L., Chawla, S. (Dec. 7, 2015)
Advances in Neural Information Processing Systems 28

Optimal Greedy Diversity for Recommendation

Ashkan, A., Kveton, B., Berkovsky, S., Wen, Z. (Jul. 25, 2015)
24th International Joint Conference on Artificial Intelligence

Cascading Bandits: Learning to Rank in the Cascade Model

Kveton, B., Szepesvari, C., Wen, Z., Ashkan, A. (Jul. 6, 2015)
32nd International Conference on Machine Learning

Efficient Learning in Large-Scale Combinatorial Semi-Bandits

Wen, Z., Kveton, B., Ashkan, A. (Jul. 6, 2015)
32nd International Conference on Machine Learning

Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits

Kveton, B., Wen, Z., Ashkan, A., Szepesvari, C. (May. 9, 2015)
18th International Conference on Artificial Intelligence and Statistics

Minimal Interaction Search in Recommender Systems

Kveton, B., Berkovsky, S. (Mar. 29, 2015)
20th ACM Conference on Intelligent User Interfaces

Structured Kernel-Based Reinforcement Learning

Kveton, B., Theocharous, G. (Jul. 14, 2013)
Association for the Advancement of Artificial Intelligence (AAAI) 2013.

Kernel-Based Reinforcement Learning on Representative States

Kveton, B., Theocharous, G. (Jul. 14, 2012)
Association for the Advancement of Artificial Intelligence (AAAI) 2012.