Ofer Meshi's Homepage
I am a Research Scientist at Google. Previously I was a Research Assistant Professor at the Toyota Technological Institute at Chicago, a philanthropically endowed academic computer science institute at the University of Chicago.
I obtained my Ph.D. and M.Sc. in Computer Science from the Hebrew University of Jerusalem, where I worked with Amir Globerson and Nir Friedman. My B.Sc. in Computer Science is from Tel Aviv University.
My research is in Machine learning and optimization. In particular, I am interested in finding efficient algorithms for: recommender systems, preference elicitation, structured output prediction, probabilistic graphical models, reinforcement learning and other related problems.
Contact Information
Email:
Publications
Model-Free Preference Elicitation
C. Martin, C. Boutilier, O. Meshi
In NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World.Preference Elicitation for Music Recommendations
O. Meshi, J. Feldman, L. Yang, B. Scheetz, Y. Cai, M. Bateni, C. Salisbury, V. Aggarwal, C. Boutilier
In ICML 2023 Workshop on Preference Learning.Overcoming Prior Misspecification in Online Learning to Rank
J. Azizi, O. Meshi, M. Zoghi, M. Karimzadehgan
In Artificial Intelligence and Statistics (AISTATS) 2023.On the Value of Prior in Online Learning to Rank
B. Kveton, O. Meshi, Z. Qin, M. Zoghi
In Artificial Intelligence and Statistics (AISTATS) 2022.Advantage Amplification in Slowly Evolving Latent-State Environments
M. Mladenov, O. Meshi, J. Ooi, D. Schuurmans, C. Boutilier
In International Joint Conference on Artificial Intelligence (IJCAI) 2019.Train and Test Tightness of LP Relaxations in Structured Prediction [external link]
O. Meshi, B. London, A. Weller, D. Sontag
Journal of Machine Learning Research (JMLR) 2019.MAP Estimation: Linear Programming Relaxation and Message-Passing Algorithms (Book chapter)
O. Meshi and A. Schwing
Handbook of Graphical Models, editors M. Maathuis, M. Drton, S. Lauritzen, M. Wainwright, CRC Press 2018.Seq2Slate: Re-ranking and Slate Optimization with RNNs
I. Bello, S. Kulkarni, S. Jain, C. Boutilier, E. Chi, E. Eban, X. Luo, A. Mackey, O. Meshi
ArXiv Preprint 2018.
[short version] in ICML workshop on Negative Dependence in ML (2019)Deep Structured Prediction via Nonlinear Output Transforms
C. Graber, O. Meshi, A. Schwing
In Neural Information Processing Systems (NeurIPS) 2018.Planning and Learning with Stochastic Action Sets [Extended arXiv version]
C. Boutilier, A. Cohen, A. Hassidim, Y. Mansour, O. Meshi, M. Mladenov, D. Schuurmans
In International Joint Conference on Artificial Intelligence (IJCAI) 2018.Asynchronous Parallel Coordinate Minimization for MAP Inference
O. Meshi and A. Schwing
In Neural Information Processing Systems (NIPS) 2017.Logistic Markov Decision Processes
M. Mladenov, C. Boutilier, D. Schuurmans, O. Meshi, G. Elidan, T. Lu
In International Joint Conference on Artificial Intelligence (IJCAI) 2017.Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes
D. Garber and O. Meshi
In Neural Information Processing Systems (NIPS) 2016.Bounding the Integrality Distance of LP Relaxations for Structured Prediction
B. London, O. Meshi, A. Weller
NIPS Workshop on Optimization for Machine Learning, 2016.Train and Test Tightness of LP Relaxations in Structured Prediction
O. Meshi, M. Mahdavi, A. Weller, D. Sontag
International Conference on Machine Learning (ICML) 2016.Fast and Scalable Structural SVM with Slack Rescaling
H. Choi, O. Meshi, N. Srebro
Artificial Intelligence and Statistics (AISTATS) 2016.On the Tightness of LP Relaxations for Structured Prediction
O. Meshi, M. Mahdavi, D. Sontag
NIPS Workshop on Optimization for Machine Learning, 2015.Smooth and Strong: MAP Inference with Linear Convergence
O. Meshi, M. Mahdavi, A. Schwing
Neural Information Processing Systems (NIPS) 2015.Efficient Training of Structured SVMs via Soft Constraints
O. Meshi, N. Srebro, T. Hazan
Artificial Intelligence and Statistics (AISTATS) 2015.Smoothed Coordinate Descent for MAP Inference (Book chapter)
O. Meshi, T. Jaakkola, A. Globerson
Advanced Structured Prediction, editors S. Nowozin, P. V. Gehler, J. Jancsary, C. Lampert, MIT Press 2014.Learning Structured Models with the AUC Loss and Its Generalizations (supplementary)
N. Rosenfeld, O. Meshi, D. Tarlow, A. Globerson
Artificial Intelligence and Statistics (AISTATS) 2014.Learning Max-Margin Tree Predictors
O. Meshi, E. Eban, G. Elidan, A. Globerson
Uncertainty in Artificial Intelligence (UAI) 2013.Convergence Rate Analysis of MAP Coordinate Minimization Algorithms (supplementary)
O. Meshi, T. Jaakkola, A. Globerson
Neural Information Processing Systems (NIPS) 2012.An Alternating Direction Method for Dual MAP LP Relaxation
O. Meshi and A. Globerson
European Conference on Machine Learning (ECML PKDD) 2011.More data means less inference: A pseudo-max approach to structured learning (supplementary)
D. Sontag, O. Meshi, T. Jaakkola, A. Globerson
Neural Information Processing Systems (NIPS) 2010.Learning Efficiently with Approximate Inference via Dual Losses
O. Meshi, D. Sontag, T. Jaakkola and A. Globerson
International Conference on Machine Learning (ICML) 2010.FastInf: An Efficient Approximate Inference Library
A. Jaimovich, O. Meshi, I. McGraw, G. Elidan
Journal of Machine Learning Research (JMLR), 11:1733-1736, 2010.Convexifying the Bethe Free Energy
O. Meshi, A. Jaimovich, A. Globerson and N. Friedman
Uncertainty in Artificial Intelligence (UAI) 2009.Template Based Inference in Symmetric Relational Markov Random Fields
A. Jaimovich, O. Meshi and N. Friedman
Uncertainty in Artificial Intelligence (UAI) 2007.Evolutionary Conservation and over-representation of functionally enriched network patterns in the yeast regulatory network
O. Meshi, T. Shlomi and E. Ruppin
BMC Systems Biology, 1:1, 2007.
Theses
Efficient Methods for Learning and Inference in Structured Output Prediction
O. Meshi, PhD dissertation, 2013.Learning Symmetric Relational Markov Random Fields
O. Meshi, MSc dissertation, 2007.