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.
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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
Density-based User Representation through Gaussian Process Regression for Multi-interest Personalized Retrieval
Haolun Wu
Xue Liu
Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS-24), Vancouver (2024)
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)
Seq2Slate: Re-ranking and Slate Optimization with RNNs
Irwan Bello
Sagar Jain
Alan Mackey
arXiv (2019)
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
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
