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Ofer Meshi
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2020 – today
- 2024
- [c22]Carlos Martin, Craig Boutilier, Ofer Meshi, Tuomas Sandholm:
Model-Free Preference Elicitation. IJCAI 2024: 3493-3503 - [c21]Chih-Wei Hsu
, Martin Mladenov
Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies. SIGIR 2024: 2925-2929 - [i14]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. CoRR abs/2409.17436 (2024) - 2023
- [c20]Javad Azizi, Ofer Meshi, Masrour Zoghi, Maryam Karimzadehgan:
Overcoming Prior Misspecification in Online Learning to Rank. AISTATS 2023: 594-614 - [i13]Javad Azizi, Ofer Meshi, Masrour Zoghi, Maryam Karimzadehgan:
Overcoming Prior Misspecification in Online Learning to Rank. CoRR abs/2301.10651 (2023) - [i12]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 abs/2310.20091 (2023) - 2022
- [c19]Branislav Kveton, Ofer Meshi, Masrour Zoghi, Zhen Qin:
On the Value of Prior in Online Learning to Rank. AISTATS 2022: 6880-6892
2010 – 2019
- 2019
- [j3]Ofer Meshi, Ben London, Adrian Weller, David A. Sontag:
Train and Test Tightness of LP Relaxations in Structured Prediction. J. Mach. Learn. Res. 20: 13:1-13:34 (2019) - [c18]Martin Mladenov, Ofer Meshi, Jayden Ooi, Dale Schuurmans, Craig Boutilier:
Advantage Amplification in Slowly Evolving Latent-State Environments. IJCAI 2019: 3165-3172 - [i11]Chih-Wei Hsu, Branislav Kveton, Ofer Meshi, Martin Mladenov, Csaba Szepesvári:
Empirical Bayes Regret Minimization. CoRR abs/1904.02664 (2019) - [i10]Martin Mladenov, Ofer Meshi, Jayden Ooi, Dale Schuurmans, Craig Boutilier:
Advantage Amplification in Slowly Evolving Latent-State Environments. CoRR abs/1905.13559 (2019) - 2018
- [c17]Craig Boutilier, Alon Cohen, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, Dale Schuurmans:
Planning and Learning with Stochastic Action Sets. IJCAI 2018: 4674-4682 - [c16]Colin Graber, Ofer Meshi, Alexander G. Schwing:
Deep Structured Prediction with Nonlinear Output Transformations. NeurIPS 2018: 6323-6334 - [i9]Craig Boutilier, Alon Cohen, Amit Daniely, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, Dale Schuurmans:
Planning and Learning with Stochastic Action Sets. CoRR abs/1805.02363 (2018) - [i8]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 abs/1810.02019 (2018) - [i7]Colin Graber, Ofer Meshi, Alexander G. Schwing:
Deep Structured Prediction with Nonlinear Output Transformations. CoRR abs/1811.00539 (2018) - 2017
- [c15]Martin Mladenov, Craig Boutilier, Dale Schuurmans, Ofer Meshi, Gal Elidan, Tyler Lu:
Logistic Markov Decision Processes. IJCAI 2017: 2486-2493 - [c14]Ofer Meshi, Alexander G. Schwing:
Asynchronous Parallel Coordinate Minimization for MAP Inference. NIPS 2017: 5734-5744 - 2016
- [c13]Heejin Choi, Ofer Meshi, Nathan Srebro:
Fast and Scalable Structural SVM with Slack Rescaling. AISTATS 2016: 667-675 - [c12]Ofer Meshi, Mehrdad Mahdavi, Adrian Weller, David A. Sontag:
Train and Test Tightness of LP Relaxations in Structured Prediction. ICML 2016: 1776-1785 - [c11]Dan Garber, Ofer Meshi:
Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes. NIPS 2016: 1001-1009 - [i6]Dan Garber, Ofer Meshi:
Linear-memory and Decomposition-invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes. CoRR abs/1605.06492 (2016) - 2015
- [c10]Ofer Meshi, Nathan Srebro, Tamir Hazan:
Efficient Training of Structured SVMs via Soft Constraints. AISTATS 2015 - [c9]Ofer Meshi, Mehrdad Mahdavi, Alexander G. Schwing:
Smooth and Strong: MAP Inference with Linear Convergence. NIPS 2015: 298-306 - [i5]Heejin Choi, Ofer Meshi, Nathan Srebro:
Fast and Scalable Structural SVM with Slack Rescaling. CoRR abs/1510.06002 (2015) - [i4]Ofer Meshi, Mehrdad Mahdavi, David A. Sontag:
On the Tightness of LP Relaxations for Structured Prediction. CoRR abs/1511.01419 (2015) - 2014
- [c8]Nir Rosenfeld, Ofer Meshi, Daniel Tarlow, Amir Globerson:
Learning Structured Models with the AUC Loss and Its Generalizations. AISTATS 2014: 841-849 - 2013
- [b1]Ofer Meshi:
Efficient methods for learning and inference in structured output prediction (עם תקציר בעברית ושער נוסף: שיטות יעילות ללמידה והסקה בבעיות חיזוי מובנה.). Hebrew University of Jerusalem, Israel, 2013 - [c7]Ofer Meshi, Elad Eban, Gal Elidan, Amir Globerson:
Learning Max-Margin Tree Predictors. UAI 2013 - [i3]Ofer Meshi, Elad Eban, Gal Elidan, Amir Globerson:
Learning Max-Margin Tree Predictors. CoRR abs/1309.6847 (2013) - 2012
- [c6]Ofer Meshi, Tommi S. Jaakkola, Amir Globerson:
Convergence Rate Analysis of MAP Coordinate Minimization Algorithms. NIPS 2012: 3023-3031 - [i2]Ofer Meshi, Ariel Jaimovich, Amir Globerson, Nir Friedman:
Convexifying the Bethe Free Energy. CoRR abs/1205.2624 (2012) - [i1]Ariel Jaimovich, Ofer Meshi, Nir Friedman:
Template Based Inference in Symmetric Relational Markov Random Fields. CoRR abs/1206.5276 (2012) - 2011
- [c5]Ofer Meshi, Amir Globerson:
An Alternating Direction Method for Dual MAP LP Relaxation. ECML/PKDD (2) 2011: 470-483 - 2010
- [j2]Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elidan:
FastInf: An Efficient Approximate Inference Library. J. Mach. Learn. Res. 11: 1733-1736 (2010) - [c4]Ofer Meshi, David A. Sontag, Tommi S. Jaakkola, Amir Globerson:
Learning Efficiently with Approximate Inference via Dual Losses. ICML 2010: 783-790 - [c3]David A. Sontag, Ofer Meshi, Tommi S. Jaakkola, Amir Globerson:
More data means less inference: A pseudo-max approach to structured learning. NIPS 2010: 2181-2189
2000 – 2009
- 2009
- [c2]Ofer Meshi, Ariel Jaimovich, Amir Globerson, Nir Friedman:
Convexifying the Bethe Free Energy. UAI 2009: 402-410 - 2007
- [j1]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: 1 (2007) - [c1]Ariel Jaimovich, Ofer Meshi, Nir Friedman:
Template Based Inference in Symmetric Relational Markov Random Fields. UAI 2007: 191-199
Coauthor Index
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last updated on 2024-10-21 20:31 CEST by the dblp team
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