| Probabilistic theorem proving V Gogate, P Domingos Communications of the ACM 59 (7), 107-115, 2016 | 250 | 2016 |
| A complete anytime algorithm for treewidth V Gogate, R Dechter arXiv preprint arXiv:1207.4109, 2012 | 206 | 2012 |
| SampleSearch: Importance sampling in presence of determinism V Gogate, R Dechter Artificial Intelligence 175 (2), 694-729, 2011 | 118 | 2011 |
| Cutset networks: A simple, tractable, and scalable approach for improving the accuracy of chow-liu trees T Rahman, P Kothalkar, V Gogate Machine Learning and Knowledge Discovery in Databases: European Conference …, 2014 | 94 | 2014 |
| Lifted inference seen from the other side: The tractable features A Jha, V Gogate, A Meliou, D Suciu Advances in Neural Information Processing Systems 23, 2010 | 83 | 2010 |
| Join-graph propagation algorithms R Mateescu, K Kask, V Gogate, R Dechter Journal of Artificial Intelligence Research 37, 279-328, 2010 | 76 | 2010 |
| Relieving the computational bottleneck: Joint inference for event extraction with high-dimensional features D Venugopal, C Chen, V Gogate, V Ng Proceedings of the 2014 Conference on Empirical Methods in Natural Language …, 2014 | 74 | 2014 |
| Approximate counting by sampling the backtrack-free search space V Gogate, R Dechter AAAI, 198-203, 2007 | 72 | 2007 |
| A new algorithm for sampling csp solutions uniformly at random V Gogate, R Dechter Principles and Practice of Constraint Programming-CP 2006: 12th …, 2006 | 64 | 2006 |
| On lifting the gibbs sampling algorithm D Venugopal, V Gogate Advances in Neural Information Processing Systems 25, 2012 | 63 | 2012 |
| Approximate inference algorithms for hybrid bayesian networks with discrete constraints V Gogate, R Dechter arXiv preprint arXiv:1207.1385, 2012 | 62 | 2012 |
| Learning efficient Markov networks V Gogate, W Webb, P Domingos Advances in neural information processing systems 23, 2010 | 54 | 2010 |
| Samplesearch: A scheme that searches for consistent samples V Gogate, R Dechter Artificial Intelligence and Statistics, 147-154, 2007 | 54 | 2007 |
| Evidence-based clustering for scalable inference in markov logic D Venugopal, V Gogate Machine Learning and Knowledge Discovery in Databases: European Conference …, 2014 | 52 | 2014 |
| Counting-based look-ahead schemes for constraint satisfaction K Kask, R Dechter, V Gogate Principles and Practice of Constraint Programming–CP 2004: 10th …, 2004 | 50 | 2004 |
| Merging Strategies for Sum-Product Networks: From Trees to Graphs. T Rahman, V Gogate UAI, 2016 | 46 | 2016 |
| Modeling transportation routines using hybrid dynamic mixed networks V Gogate, R Dechter, B Bidyuk, C Rindt, J Marca arXiv preprint arXiv:1207.1384, 2012 | 46 | 2012 |
| Advances in lifted importance sampling V Gogate, A Jha, D Venugopal Proceedings of the AAAI Conference on Artificial Intelligence 26 (1), 1910-1916, 2012 | 46 | 2012 |
| Lifted MAP inference for Markov logic networks S Sarkhel, D Venugopal, P Singla, V Gogate Artificial Intelligence and Statistics, 859-867, 2014 | 45 | 2014 |
| Joint inference for event coreference resolution J Lu, D Venugopal, V Gogate, V Ng Proceedings of COLING 2016, the 26th International Conference on …, 2016 | 43 | 2016 |
Rina DechterUniversity of California, IrvineVerified email at ics.uci.edu
