Ofer Meshi

Ofer Meshi

Ofer Meshi is a Research Scientist at Google. Prior to joining Google, he was a Research Assistant Professor at the Toyota Technological Institute at Chicago. Before that he obtained a Ph.D. and an M.Sc. in Computer Science from the Hebrew University of Jerusalem and a B.Sc. in Computer Science from Tel Aviv University. Ofer's research interests are in machine learning and optimization. In particular, he seeks efficient algorithms for: structured output prediction, probabilistic graphical models, reinforcement learning and other related problems. More info and previous publications can be found in his homepage.

Research Areas

Authored Publications
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    Google
Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies
Martin Mladenov
James Pine
Hubert Pham
Shane Li
Xujian Liang
Anton Polishko
Li Yang
Ben Scheetz
Proceedings of he 47th International ACM/SIGIR Conference on Research and Development in Information Retrieval (SIGIR-24), Washington, DC (2024), pp. 2925-2929
Model-Free Preference Elicitation
Carlos Martin
Tuomas Sandholm
Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI-24), Jeju, South Korea (2024), pp. 3493-3503
Overcoming Prior Misspecification in Online Learning to Rank
Mohammadjavad Azizi
International Conference on Artificial Intelligence and Statistics, PMLR (2023), pp. 594-614
On the Value of Prior in Online Learning to Rank
Branislav Kveton
The 25th International Conference on Artificial Intelligence and Statistics (2022)
Advantage Amplification in Slowly Evolving Latent-State Environments
Martin Mladenov
Proceedings of the Twenty-eighth International Joint Conference on Artificial Intelligence (IJCAI-19), Macau, China (2019), pp. 3165-3172
Planning and Learning with Stochastic Action Sets
Martin Mladenov
Proceedings of the Twenty-seventh International Joint Conference on Artificial Intelligence (IJCAI-18), Stockholm (2018), pp. 4674-4682
Asynchronous Parallel Coordinate Minimization for MAP Inference
Alexander G. Schwing
Advances in Neural Information Processing Systems (NIPS) 30 (2017)
Approximate Linear Programming for Logistic Markov Decision Processes
Martin Mladenov
Tyler Lu
Proceedings of the Twenty-sixth International Joint Conference on Artificial Intelligence (IJCAI-17), Melbourne, Australia (2017), pp. 2486-2493