Ofer Meshi

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 24

Top Topics

Structured Prediction

Gaussian Process Regression

Top Venues

Publications

Chih-Wei Hsu, Martin Mladenov, Ofer Meshi, James Pine, Hubert Pham, Shane Li, Xujian Liang, Anton Polishko, Li Yang, Ben Scheetz, Craig Boutilier
Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies. SIGIR (2024)
Javad Azizi, Ofer Meshi, Masrour Zoghi, Maryam Karimzadehgan
Overcoming Prior Misspecification in Online Learning to Rank. AISTATS (2023)
Haolun Wu, Ofer Meshi, Masrour Zoghi, Fernando Diaz, Xue Liu, Craig Boutilier, Maryam Karimzadehgan
Density-based User Representation through Gaussian Process Regression for Multi-interest Personalized Retrieval. CoRR (2023)
Javad Azizi, Ofer Meshi, Masrour Zoghi, Maryam Karimzadehgan
Overcoming Prior Misspecification in Online Learning to Rank. CoRR (2023)
Branislav Kveton, Ofer Meshi, Masrour Zoghi, Zhen Qin
On the Value of Prior in Online Learning to Rank. AISTATS (2022)
Chih-Wei Hsu, Branislav Kveton, Ofer Meshi, Martin Mladenov, Csaba Szepesvári
Empirical Bayes Regret Minimization. CoRR (2019)
Martin Mladenov, Ofer Meshi, Jayden Ooi, Dale Schuurmans, Craig Boutilier
Advantage Amplification in Slowly Evolving Latent-State Environments. CoRR (2019)
Martin Mladenov, Ofer Meshi, Jayden Ooi, Dale Schuurmans, Craig Boutilier
Advantage Amplification in Slowly Evolving Latent-State Environments. IJCAI (2019)
Ofer Meshi, Ben London, Adrian Weller, David A. Sontag
Train and Test Tightness of LP Relaxations in Structured Prediction. J. Mach. Learn. Res. 20 (2019)
Craig Boutilier, Alon Cohen, Amit Daniely, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, Dale Schuurmans
Planning and Learning with Stochastic Action Sets. CoRR (2018)
Craig Boutilier, Alon Cohen, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, Dale Schuurmans
Planning and Learning with Stochastic Action Sets. IJCAI (2018)
Irwan Bello, Sayali Kulkarni, Sagar Jain, Craig Boutilier, Ed Huai-hsin Chi, Elad Eban, Xiyang Luo, Alan Mackey, Ofer Meshi
Seq2Slate: Re-ranking and Slate Optimization with RNNs. CoRR (2018)
Colin Graber, Ofer Meshi, Alexander G. Schwing
Deep Structured Prediction with Nonlinear Output Transformations. CoRR (2018)
Colin Graber, Ofer Meshi, Alexander G. Schwing
Deep Structured Prediction with Nonlinear Output Transformations. NeurIPS (2018)
Martin Mladenov, Craig Boutilier, Dale Schuurmans, Ofer Meshi, Gal Elidan, Tyler Lu
Logistic Markov Decision Processes. IJCAI (2017)
Ofer Meshi, Alexander G. Schwing
Asynchronous Parallel Coordinate Minimization for MAP Inference. NIPS (2017)
Ofer Meshi, Mehrdad Mahdavi, Adrian Weller, David A. Sontag
Train and Test Tightness of LP Relaxations in Structured Prediction. ICML (2016)
Dan Garber, Ofer Meshi
Linear-memory and Decomposition-invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes. CoRR (2016)
Heejin Choi, Ofer Meshi, Nathan Srebro
Fast and Scalable Structural SVM with Slack Rescaling. AISTATS (2016)
Dan Garber, Ofer Meshi
Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes. NIPS (2016)
Ofer Meshi, Mehrdad Mahdavi, Alexander G. Schwing
Smooth and Strong: MAP Inference with Linear Convergence. NIPS (2015)
Ofer Meshi, Mehrdad Mahdavi, David A. Sontag
On the Tightness of LP Relaxations for Structured Prediction. CoRR (2015)
Ofer Meshi, Nathan Srebro, Tamir Hazan
Efficient Training of Structured SVMs via Soft Constraints. AISTATS (2015)
Heejin Choi, Ofer Meshi, Nathan Srebro
Fast and Scalable Structural SVM with Slack Rescaling. CoRR (2015)
Nir Rosenfeld, Ofer Meshi, Daniel Tarlow, Amir Globerson
Learning Structured Models with the AUC Loss and Its Generalizations. AISTATS (2014)
Ofer Meshi, Elad Eban, Gal Elidan, Amir Globerson
Learning Max-Margin Tree Predictors. CoRR (2013)
Ofer Meshi, Elad Eban, Gal Elidan, Amir Globerson
Learning Max-Margin Tree Predictors. UAI (2013)
Ofer Meshi, Ariel Jaimovich, Amir Globerson, Nir Friedman
Convexifying the Bethe Free Energy CoRR (2012)
Ariel Jaimovich, Ofer Meshi, Nir Friedman
Template Based Inference in Symmetric Relational Markov Random Fields CoRR (2012)
Ofer Meshi, Tommi S. Jaakkola, Amir Globerson
Convergence Rate Analysis of MAP Coordinate Minimization Algorithms. NIPS (2012)
Ofer Meshi, Amir Globerson
An Alternating Direction Method for Dual MAP LP Relaxation. ECML/PKDD (2) (2011)
Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elidan
FastInf: An Efficient Approximate Inference Library. J. Mach. Learn. Res. 11 (2010)
David A. Sontag, Ofer Meshi, Tommi S. Jaakkola, Amir Globerson
More data means less inference: A pseudo-max approach to structured learning. NIPS (2010)
Ofer Meshi, David A. Sontag, Tommi S. Jaakkola, Amir Globerson
Learning Efficiently with Approximate Inference via Dual Losses. ICML (2010)
Ofer Meshi, Ariel Jaimovich, Amir Globerson, Nir Friedman
Convexifying the Bethe Free Energy. UAI (2009)
Ofer Meshi, Tomer Shlomi, Eytan Ruppin
Evolutionary conservation and over-representation of functionally enriched network patterns in the yeast regulatory network. BMC Syst. Biol. 1 (2007)
Ariel Jaimovich, Ofer Meshi, Nir Friedman
Template Based Inference in Symmetric Relational Markov Random Fields. UAI (2007)