| Learning efficiently with approximate inference via dual losses O Meshi, D Sontag, T Jaakkola, A Globerson International Machine Learning Society, 2010 | 87 | 2010 |
| An alternating direction method for dual MAP LP relaxation O Meshi, A Globerson Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011 | 81 | 2011 |
| Convexifying the Bethe free energy O Meshi, A Jaimovich, A Globerson, N Friedman arXiv preprint arXiv:1205.2624, 2012 | 59 | 2012 |
| Template based inference in symmetric relational Markov random fields A Jaimovich, O Meshi, N Friedman arXiv preprint arXiv:1206.5276, 2012 | 54 | 2012 |
| Linear-memory and decomposition-invariant linearly convergent conditional gradient algorithm for structured polytopes D Garber, O Meshi Advances in neural information processing systems 29, 2016 | 52 | 2016 |
| Smooth and strong: Map inference with linear convergence O Meshi, M Mahdavi, A Schwing Advances in Neural Information Processing Systems 28, 2015 | 39 | 2015 |
| Convergence rate analysis of MAP coordinate minimization algorithms O Meshi, A Globerson, T Jaakkola Advances in Neural Information Processing Systems 25, 2012 | 38 | 2012 |
| Seq2slate: Re-ranking and slate optimization with rnns I Bello, S Kulkarni, S Jain, C Boutilier, E Chi, E Eban, X Luo, A Mackey, ... arXiv preprint arXiv:1810.02019, 2018 | 35 | 2018 |
| More data means less inference: A pseudo-max approach to structured learning D Sontag, O Meshi, T Jaakkola, A Globerson Neural Information Processing Systems Foundation, 2010 | 31 | 2010 |
| Learning structured models with the AUC loss and its generalizations N Rosenfeld, O Meshi, D Tarlow, A Globerson Artificial Intelligence and Statistics, 841-849, 2014 | 25 | 2014 |
| Deep structured prediction with nonlinear output transformations C Graber, O Meshi, A Schwing Advances in Neural Information Processing Systems 31, 2018 | 22 | 2018 |
| 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 | 22 | 2016 |
| Planning and learning with stochastic action sets C Boutilier, A Cohen, A Daniely, A Hassidim, Y Mansour, O Meshi, ... arXiv preprint arXiv:1805.02363, 2018 | 19 | 2018 |
| Evolutionary conservation and over-representation of functionally enriched network patterns in the yeast regulatory network O Meshi, T Shlomi, E Ruppin BMC systems biology 1, 1-7, 2007 | 19 | 2007 |
| Empirical Bayes regret minimization CW Hsu, B Kveton, O Meshi, M Mladenov, C Szepesvari arXiv preprint arXiv:1904.02664, 2019 | 14 | 2019 |
| Efficient training of structured svms via soft constraints O Meshi, N Srebro, T Hazan Artificial Intelligence and Statistics, 699-707, 2015 | 14 | 2015 |
| FastInf: An efficient approximate inference library A Jaimovich, O Meshi, I McGraw, G Elidan The Journal of Machine Learning Research 11, 1733-1736, 2010 | 14 | 2010 |
| Asynchronous parallel coordinate minimization for map inference O Meshi, A Schwing Advances in Neural Information Processing Systems 30, 2017 | 9 | 2017 |
| Learning Max-Margin Tree Predictors O Meshi, E Eban, G Elidan, A Globerson Uncertainty in Artificial Intelligence (UAI), 2013 | 8 | 2013 |
| Approximate linear programming for logistic Markov decision processes M Mladenov, C Boutilier, D Schuurmans, G Elidan, O Meshi, T Lu | 7 | 2017 |
Amir GlobersonTel Aviv UniversityVahvistettu sähköpostiosoite verkkotunnuksessa post.tau.ac.il
