Template Based Inference in Symmetric Relational Markov Random Fields. - Ariel Jaimovich; Ofer Meshi; Nir Friedman (UAI, 2007)

Evolutionary conservation and over-representation of functionally enriched network patterns in the yeast regulatory network. - Ofer Meshi; Tomer Shlomi; Eytan Ruppin (BMC Syst. Biol., 2007)

Convexifying the Bethe Free Energy. - Ofer Meshi; Ariel Jaimovich; Amir Globerson; Nir Friedman (UAI, 2009)

Learning Efficiently with Approximate Inference via Dual Losses. - Ofer Meshi; David A. Sontag; Tommi S. Jaakkola; Amir Globerson (ICML, 2010)

More data means less inference: A pseudo-max approach to structured learning. - David A. Sontag; Ofer Meshi; Tommi S. Jaakkola; Amir Globerson (NIPS, 2010)

FastInf: An Efficient Approximate Inference Library. - Ariel Jaimovich; Ofer Meshi; Ian McGraw; Gal Elidan (J. Mach. Learn. Res., 2010)

An Alternating Direction Method for Dual MAP LP Relaxation. - Ofer Meshi; Amir Globerson (ECML/PKDD (2), 2011)

Convergence Rate Analysis of MAP Coordinate Minimization Algorithms. - Ofer Meshi; Tommi S. Jaakkola; Amir Globerson (NIPS, 2012)

Learning Max-Margin Tree Predictors. - Ofer Meshi; Elad Eban; Gal Elidan; Amir Globerson (UAI, 2013)

Efficient methods for learning and inference in structured output prediction (עם תקציר בעברית ושער נוסף: שיטות יעילות ללמידה והסקה בבעיות חיזוי מובנה.). - Ofer Meshi (2013)

Learning Structured Models with the AUC Loss and Its Generalizations. - Nir Rosenfeld; Ofer Meshi; Daniel Tarlow; Amir Globerson (AISTATS, 2014)

Efficient Training of Structured SVMs via Soft Constraints. - Ofer Meshi; Nathan Srebro; Tamir Hazan (AISTATS, 2015)

Smooth and Strong: MAP Inference with Linear Convergence. - Ofer Meshi; Mehrdad Mahdavi; Alexander G. Schwing (NIPS, 2015)

On the Tightness of LP Relaxations for Structured Prediction. - Ofer Meshi; Mehrdad Mahdavi; David A. Sontag (CoRR, 2015)

Fast and Scalable Structural SVM with Slack Rescaling. - Heejin Choi; Ofer Meshi; Nathan Srebro (AISTATS, 2016)

Train and Test Tightness of LP Relaxations in Structured Prediction. - Ofer Meshi; Mehrdad Mahdavi; Adrian Weller; David A. Sontag (ICML, 2016)

Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes. - Dan Garber; Ofer Meshi (NIPS, 2016)

Logistic Markov Decision Processes. - Martin Mladenov; Craig Boutilier; Dale Schuurmans; Ofer Meshi; Gal Elidan; Tyler Lu (IJCAI, 2017)

Asynchronous Parallel Coordinate Minimization for MAP Inference. - Ofer Meshi; Alexander G. Schwing (NIPS, 2017)

Planning and Learning with Stochastic Action Sets. - Craig Boutilier; Alon Cohen; Avinatan Hassidim; Yishay Mansour; Ofer Meshi; Martin Mladenov; Dale Schuurmans (IJCAI, 2018)

Deep Structured Prediction with Nonlinear Output Transformations. - Colin Graber; Ofer Meshi; Alexander G. Schwing (NeurIPS, 2018)

Planning and Learning with Stochastic Action Sets. - Craig Boutilier; Alon Cohen; Amit Daniely; Avinatan Hassidim; Yishay Mansour; Ofer Meshi; Martin Mladenov; Dale Schuurmans (CoRR, 2018)

Seq2Slate: Re-ranking and Slate Optimization with RNNs. - Irwan Bello; Sayali Kulkarni; Sagar Jain; Craig Boutilier; Ed Huai-hsin Chi; Elad Eban; Xiyang Luo; Alan Mackey; Ofer Meshi (CoRR, 2018)

Advantage Amplification in Slowly Evolving Latent-State Environments. - Martin Mladenov; Ofer Meshi; Jayden Ooi; Dale Schuurmans; Craig Boutilier (IJCAI, 2019)

Empirical Bayes Regret Minimization. - Chih-Wei Hsu; Branislav Kveton; Ofer Meshi; Martin Mladenov; Csaba Szepesvári (CoRR, 2019)

Train and Test Tightness of LP Relaxations in Structured Prediction. - Ofer Meshi; Ben London 001; Adrian Weller; David A. Sontag (J. Mach. Learn. Res., 2019)

On the Value of Prior in Online Learning to Rank. - Branislav Kveton; Ofer Meshi; Masrour Zoghi; Zhen Qin (AISTATS, 2022)

Overcoming Prior Misspecification in Online Learning to Rank. - Javad Azizi; Ofer Meshi; Masrour Zoghi; Maryam Karimzadehgan (AISTATS, 2023)

Density-based User Representation through Gaussian Process Regression for Multi-interest Personalized Retrieval. - Haolun Wu; Ofer Meshi; Masrour Zoghi; Fernando Diaz 001; Xue Liu 001; Craig Boutilier; Maryam Karimzadehgan (CoRR, 2023)

Model-Free Preference Elicitation. - Carlos Martin 001; Craig Boutilier; Ofer Meshi; Tuomas Sandholm (IJCAI, 2024)

Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval. - Haolun Wu; Ofer Meshi; Masrour Zoghi; Fernando Diaz 001; Xue (Steve) Liu; Craig Boutilier; Maryam Karimzadehgan (NeurIPS, 2024)

Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies. - Chih-Wei Hsu; Martin Mladenov; Ofer Meshi; James Pine; Hubert Pham; Shane Li; Xujian Liang; Anton Polishko; Li Yang; Ben Scheetz; Craig Boutilier (SIGIR, 2024)