Imprisoned in a cage on a castle in Northern Russia.

Branislav Kveton

is an AI scientist at Intel Research in Santa Clara. His research interests are stochastic reasoning under uncertainty and machine learning. In particular, Brano is interested in adaptive and efficient algorithms for solving real-world problems. He is a part of a new Intel's initiative Everyday Sensing and Perception (ESP), whose goal is to develop a context recognition system that is 90 percent accurate over 90 percent of the day.

Before joining Intel Research in 2006, Brano was a graduate student in the Intelligent Systems Program at the University of Pittsburgh. His graduate advisor was Milos Hauskrecht.

Publications

  1. Branislav Kveton and Milos Hauskrecht. Partitioned Linear Programming Approximations for MDPs. In Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, Helsinki, Finland, July 2008.

    [Paper] [BibTeX]

  2. Branislav Kveton, Jia Yuan Yu, Georgios Theocharous, and Shie Mannor. Online Learning with Expert Advice and Finite-Horizon Constraints. In Proceedings of the 23rd National Conference on Artificial Intelligence, Chicago, Illinois, July 2008.

    [Paper] [BibTeX] Acceptance rate 227 / 937 = 24%.

  3. Branislav Kveton, Jia Yuan Yu, Georgios Theocharous, and Shie Mannor. A Lazy Approach to Online Learning with Constraints. In Proceedings of the 10th International Symposium on Artificial Intelligence and Mathematics, Fort Lauderdale, Florida, January 2008.

    [Paper] [BibTeX]

  4. Branislav Kveton, Prashant Gandhi, Georgios Theocharous, Shie Mannor, Barbara Rosario, and Nilesh Shah. Adaptive Timeout Policies for Fast Fine-Grained Power Management. In Proceedings of the 22nd National Conference on Artificial Intelligence, pages 1795-1800, Vancouver, Canada, July 2007.

    [Paper] [BibTeX]

  5. Branislav Kveton. Planning in Hybrid Structured Stochastic Domains. PhD thesis, University of Pittsburgh, December 2006.

    [Paper] [BibTeX]

  6. Georgios Theocharous, Shie Mannor, Nilesh Shah, Prashant Gandhi, Branislav Kveton, Sajid Siddiqi, and Chih-Han Yu. Machine Learning for Adaptive Power Management. Intel Technology Journal 10(4), November 2006.

    [Paper] [BibTeX]

  7. Branislav Kveton, Milos Hauskrecht, and Carlos Guestrin. Solving Factored MDPs with Hybrid State and Action Variables. Journal of Artificial Intelligence Research 27, pages 153-201, October 2006.

    [Paper] [BibTeX]

  8. Branislav Kveton and Milos Hauskrecht. Learning Basis Functions in Hybrid Domains. In Proceedings of the 21st National Conference on Artificial Intelligence, pages 1161-1166, Boston, Massachusetts, July 2006.

    [Paper] [BibTeX] [Poster] Acceptance rate 236 / 774 = 30%.

  9. Denver Dash, Branislav Kveton, John Mark Agosta, Eve Schooler, Jaideep Chandrashekar, Abraham Bachrach, and Alex Newman. When Gossip is Good: Distributed Probabilistic Inference for Detection of Slow Network Intrusions. In Proceedings of the 21st National Conference on Artificial Intelligence, pages 1115-1122, Boston, Massachusetts, July 2006.

    [Paper] [BibTeX] Acceptance rate 236 / 774 = 30%.

  10. Branislav Kveton and Milos Hauskrecht. Solving Factored MDPs with Exponential-Family Transition Models. In Proceedings of the 16th International Conference on Automated Planning and Scheduling, pages 114-120, The English Lake District, Cumbria, United Kingdom, June 2006.

    [Paper] [BibTeX] Acceptance rate 33 / 99 = 33%.

  11. Milos Hauskrecht and Branislav Kveton. Approximate Linear Programming for Solving Hybrid Factored MDPs. In Proceedings of the 9th International Symposium on Artificial Intelligence and Mathematics, Fort Lauderdale, Florida, January 2006.

    [Paper] [BibTeX] Invited talk!

  12. Denver Dash, John Mark Agosta, Eve Schooler, and Branislav Kveton. Population-Based Modeling for Distributed Detection of Anomalies. In Proceedings of Intel Research Symposium, Hillsboro, Oregon, August 2005.
  13. Branislav Kveton and Milos Hauskrecht. An MCMC Approach to Solving Hybrid Factored MDPs. In Proceedings of the 19th International Joint Conference on Artificial Intelligence, pages 1346-1351, Edinburgh, Scotland, August 2005.

    [Paper] [BibTeX] Acceptance rate 240 / 1329 = 18%.

  14. Branislav Kveton and Denver Dash. Automatic Excursion Detection in Manufacturing: Preliminary Results. In Proceedings of the 18th International Florida Artificial Intelligence Research Society Conference, pages 375-380, Clearwater Beach, Florida, May 2005.

    [Paper] [BibTeX] Acceptance rate 132 / 247 = 53%.

  15. Ondrej Turza and Branislav Kveton. Decision Support Automatization in HR Management. In Proceedings of the 2nd International Conference on Human Potential Management in the Corporate Environment, pages 177-182, Zilina, Slovak Republic, March 2005.
  16. Carlos Guestrin, Milos Hauskrecht, and Branislav Kveton. Solving Factored MDPs with Continuous and Discrete Variables. In Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, pages 235-242, Banff, Canada, July 2004.

    [Paper] [BibTeX] Acceptance rate 75 / 253 = 30%.

    A shorter version is available in Carlos Guestrin, Milos Hauskrecht, and Branislav Kveton. Solving Factored MDPs with Continuous and Discrete Variables. In Proceedings of AAAI Workshop on Learning and Planning in Markov Processes - Advances and Challenges, pages 19-24, San Jose, California, July 2004.
  17. Branislav Kveton and Milos Hauskrecht. Heuristic Refinements of Approximate Linear Programming for Factored Continuous-State Markov Decision Processes. In Proceedings of the 14th International Conference on Automated Planning and Scheduling, pages 306-314, Whistler, Canada, June 2004.

    [Paper] [BibTeX] Acceptance rate 37 / 119 = 31%.

  18. Milos Hauskrecht and Branislav Kveton. Linear Program Approximations for Factored Continuous-State Markov Decision Processes. In Advances in Neural Information Processing Systems 16, pages 895-902, June 2004.

    [Paper] [BibTeX] Acceptance rate 198 / 717 = 28%.

  19. Branislav Kveton. Estimation of the Critical Value of Oriented Percolation in 1 + 1 Dimensions. Masters thesis, Comenius University, July 2001.

Workshops, Presentations, and Posters

  1. Online Learning with Constraints for Systems. Presented at NIPS Workshop on Statistical Learning Techniques for Solving Systems Problems, Whistler, Canada, December 2007.

    [Poster]

  2. Solving Constrained Optimization Problems Online. Presented at Intel Research Symposium, Portland, Oregon, August 2007.

    [Poster]

  3. Machine Learning for the Real World. Presented at Intel Developer Forum, San Francisco, California, September 2006.
  4. On the Smoothness of Linear Value Function Approximations. Presented at ICAPS Doctoral Consortium, The English Lake District, Cumbria, United Kingdom, June 2006.
  5. John Mark Agosta, Abraham Bachrach, Denver Dash, Branislav Kveton, Alex Newman, and Eve Schooler. Distributed Detection and Inference for Enterprise Networks. Presented at NIPS Workshop on Intelligence Beyond the Desktop, Whistler, Canada, December 2005.
  6. John Mark Agosta, Abraham Bachrach, Denver Dash, Branislav Kveton, Alex Newman, and Eve Schooler. Distributed Inference to Detect a Network Attack. Presented at the 4th Adaptive and Resilient Computing Security Workshop, Santa Fe, New Mexico, November 2005.
  7. Towards Solving Complex Real-World Decision Problems. Presented at UPitt Intelligent Systems AI Forum, Pittsburgh, Pennsylvania, September 2005.
  8. Towards More Realistic Models for Distributed Worm Detection. Presented at Intel Research Symposium, Hillsboro, Oregon, August 2005.

    [Poster]

  9. Planning in Complex Structured Stochastic Domains. Presented at Comenius University AI Forum, Bratislava, Slovak Republic, May 2005.
  10. Automatic Traffic Management in Complex Transportation Networks. Presented at UPitt 5th Annual Computer Science Day, Pittsburgh, Pennsylvania, February 2005.

    Best poster runner-up!

  11. Solving Factored MDPs with Continuous and Discrete Variables. Presented at UPitt Intelligent Systems AI Forum, Pittsburgh, Pennsylvania, October 2004.
  12. Heuristic Refinements of Approximate Linear Programming for Factored Continuous-State Markov Decision Processes. Presented at ICAPS Doctoral Consortium, Whistler, Canada, June 2004.
  13. Linear Program Approximations for Factored Continuous-State Markov Decision Processes. Presented at NIPS Workshop on Planning for the Real World: The Promises and Challenges of Dealing with Uncertainty, Whistler, Canada, December 2003.
  14. Linear Program Approximations for Factored Continuous-State Markov Decision Processes. Presented at UPitt Intelligent Systems AI Forum, Pittsburgh, Pennsylvania, November 2003.