Vibhav Gogate

Papers

2016

  • [ C 42 ] Jing Lu, Deepak Venugopal, Vibhav Gogate, and Vincent Ng, “Joint Inference for Multilingual Event Coreference Resolution”, Coling 2016. [PDF]

  • [ J 6 ] Vibhav Gogate and Pedro Domingos, “Probabilistic Theorem Proving”, Communications of the ACM journal, 2016. [Link]

  • [ C 41 ] Tahrima Rahman and Vibhav Gogate, “Merging Strategies for Sum-Product Networks: From Trees to Graphs”, UAI 2016. [PDF]

  • [ C 40 ] Rodrigo de Salvo Braz, Ciaran O'Reilly, Vibhav Gogate and Rina Dechter, “Probabilistic Inference Modulo Theories,” IJCAI 2016. [PDF]

  • [ C 39 ] Li Chou, Somdeb Sarkhel, Nicholas Ruozzi and Vibhav Gogate, “On Parameter Tying by Quantization”, AAAI 2016. [PDF]

  • [ C 38 ] Somdeb Sarkhel, Deepak Venugopal, Tuan Anh Pham, Parag Singla and Vibhav Gogate, “Scalable Training of Markov Logic Networks using Approximate Counting,” AAAI 2016. [PDF]

  • [ C 37 ] Tahrima Rahman and Vibhav Gogate, “Learning Ensembles of Cutset Networks,” AAAI 2016. [PDF]

2015

  • [ C 36 ] David Smith and Vibhav Gogate, “Bounding the Cost of Search-Based Lifted Inference,” NIPS 2015. [PDF].

  • [ C 35 ] Somdeb Sarkhel, Parag Singla and Vibhav Gogate, “Fast Lifted MAP Inference via Partitioning,” NIPS 2015. [PDF].

  • [ C 34 ] Happy Mittal, Anuj Mahajan, Vibhav Gogate, and Parag Singla, “Lifted Inference Rules With Constraints,” NIPS 2015. [PDF].

  • [ T 3 ] Deepak Venugopal, “Scalable Inference Techniques for Markov Logic,” Ph.D. Thesis, The University of Texas at Dallas, 2015.[PDF] (First employment: Assistant Professor, Computer Science, University of Memphis)

  • [ W 9 ] Rodrigo de Salvo Braz, Ciaran O'Reilly, Vibhav Gogate and Rina Dechter, “Probabilistic Inference Modulo Theories,” In Workshop on Hybrid Reasoning at IJCAI 2015. [PDF]

  • [ C 33 ] Deepak Venugopal, Somdeb Sarkhel and Vibhav Gogate, “Just Count the Satisfied Groundings: Scalable Local-Search and Sampling Based Inference in MLNs,” In AAAI 2015. [PDF]

  • [ T 2 ] Srikanth Doss K. R., “Lifting Iterative Join Graph Propagation,” Masters Thesis, The University of Texas at Dallas, 2015. [PDF] (First employment: Amazon).

2014

  • [ C 32 ] Somdeb Sarkhel, Deepak Venugopal, Parag Singla and Vibhav Gogate, “An Integer Polynomial Programming Based Framework for Lifted MAP Inference,” In NIPS 2014. [PDF]

  • [ C 31 ] Deepak Venugopal and Vibhav Gogate, “Scaling-up Importance Sampling for Markov Logic Networks,” In NIPS 2014. [PDF]

  • [ C 30 ] Happy Mittal, Prasoon Goyal, Vibhav Gogate and Parag Singla, “New Rules for Domain Independent Lifted MAP Inference,” In NIPS 2014. [PDF]

  • [ C 29 ] Deepak Venugopal, Chen Chen, Vibhav Gogate and Vincent Ng, “Relieving the Computational Bottleneck: Joint Inference for Event Extraction with High-Dimensional Features,” In Empirical Methods in Natural Language Processing Conference (EMNLP), 2014. [PDF]

  • [ C 28 ] Tahrima Rahman, Prasanna Kothalkar, and Vibhav Gogate, “Cutset Networks: A Simple, Tractable, and Scalable Approach for Improving the Accuracy of Chow-Liu Trees,” In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2014. [PDF]

  • [ C 27 ] Deepak Venugopal and Vibhav Gogate, “Evidence-Based Clustering for Scalable Inference in Markov Logic,” In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2014. [PDF]

  • [ C 26] David Smith and Vibhav Gogate, “Loopy Belief Propagation in the Presence of Determinism,” In 17th International Conference on Artificial Intelligence and Statistics (AISTATS), 2014. [PDF]

  • [ C 25] Somdeb Sarkhel, Deepak Venugopal, Parag Singla and Vibhav Gogate, “Lifted MAP Inference for Markov Logic Networks,” In 17th International Conference on Artificial Intelligence and Statistics (AISTATS), 2014. [PDF]

2013

  • [W 8] Somdeb Sarkhel and Vibhav Gogate, “Lifting WALKSAT-based Local Search Algorithms for MAP Inference,” In AAAI-13 Workshop on Statistical Relational Artificial Intelligence, 2013. [PDF]

  • [C 24] Deepak Venugopal and Vibhav Gogate, “Dynamic Blocking and Collapsing for Gibbs Sampling,” In 29th Conference on Uncertainty in Artificial Intelligence (UAI), 2013. [PDF]

  • [C 23] Vibhav Gogate and Pedro Domingos, “Structured Message Passing,” In 29th Conference on Uncertainty in Artificial Intelligence (UAI), 2013. [PDF]

  • [C 22] David Smith and Vibhav Gogate, “The Inclusion-Exclusion Rule and its Application to the Junction Tree Algorithm,” In 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013. [PDF]

  • [C 21] Deepak Venugopal and Vibhav Gogate, “GiSS: Combining SampleSearch and Importance Sampling for Inference in Mixed Probabilistic and Deterministic Graphical Models,” In 27th AAAI Conference on Artificial Intelligence (AAAI), 2013. [PDF]

2012

  • [W 7] Vibhav Gogate and Pedro Domingos, “Probabilistic Theorem Proving: A Unifying Approach for Inference in Probabilistic Programming,” In NIPS 2012 workshop on Probabilistic Programming. 2012. [PDF]

  • [C 20, W 7] Deepak Venugopal and Vibhav Gogate, “On Lifting the Gibbs Sampling Algorithm,” In 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012. [PDF]

  • [C 19] Vibhav Gogate, Abhay Jha and Deepak Venugopal, “Advances in Lifted Importance Sampling,” In 26th AAAI Conference on Artificial Intelligence (AAAI), 2012. [PDF]

  • [J 4] Vibhav Gogate and Rina Dechter, “Importance Sampling based Estimation over AND/OR search spaces for graphical models,” Artificial Intelligence Journal. 2012. [PDF]

2011

  • [J 3] Vibhav Gogate and Rina Dechter, “Sampling-based Lower Bounds for Counting Queries,” Intelligenza Artificiale. 2011. [PDF]

  • [J 2] Vibhav Gogate and Rina Dechter, “SampleSearch: Importance Sampling in presence of Determinism,” Artificial Intelligence Journal, 2011. [PDF]

  • [C 18] Vibhav Gogate and Pedro Domingos, “Probabilistic Theorem Proving,” In 27th Conference on Uncertainty in Artificial Intelligence (UAI), 2011. [PDF]

  • [C 17] Vibhav Gogate and Pedro Domingos, “Approximation by Quantization,” In 27th Conference on Uncertainty in Artificial Intelligence (UAI), 2011. [PDF]

2010

  • [C 16] Vibhav Gogate, William Austin Webb and Pedro Domingos, “Learning Efficient Markov networks,” In Twenty-Fourth Annual Conference on Neural Information Processing Systems (NIPS), 2010. [PDF]

  • [C 15] Abhay Jha, Vibhav Gogate, Alexandra Meliou and Dan Suciu, “Lifted Inference from the Other Side: The tractable Features,” In Twenty-Fourth Annual Conference on Neural Information Processing Systems (NIPS), 2010. [PDF]

  • [W 5 ] Vibhav Gogate and Pedro Domingos, Exploiting Logical Structure in Lifted Probabilistic Inference, In AAAI 2010 workshop on Statististical and Relational Artificial Intelligence (STAR-AI). 2010. [PDF]

  • [C 14] Vibhav Gogate and Pedro Domingos, “Formula-Based Probabilistic Inference,” In 26th Conference on Uncertainty in Artificial Intelligence (UAI), 2010. Algorithm presented in the paper was co-winner of the 2010 UAI approximate inference challenge. [PDF]

  • [C 13] Vibhav Gogate and Rina Dechter, “On Combining Graph-based Variance Reduction schemes,” In 13th International Conference on Artificial Intelligence and Statistics (AISTATS), 2010. [PDF]

  • [J 1] Robert Mateescu, Kalev Kask, Vibhav Gogate and Rina Dechter, “Join-Graph Propagation Algorithms”, Journal of Artificial Intelligence Research, 2010. Algorithm presented in the paper was co-winner of the 2010 UAI approximate inference challenge. [PDF]

2009

  • [T 1] Vibhav Gogate, “Sampling Algorithms for Probabilistic Graphical Models with Determinism,” Ph.D. thesis, University of California, Irvine, June 2009. Thesis nominated by University of California, Irvine for the ACM Doctoral Dissertation award. [PDF]

2008

  • [C 12] Vibhav Gogate and Rina Dechter, “Approximate Solution Sampling ( and Counting) on ANDOR search spaces/,” In 14th International Conference on Principles and Practice of Constraint Programming (CP), 2008. [PDF]

  • [C 11] Vibhav Gogate and Rina Dechter, “AND\OR Importance Sampling,” In 24th Conferenceon Uncertainty in Artificial Intelligence (UAI), 2008. [PDF]

  • [C 10] Vibhav Gogate and Rina Dechter, “Studies in Solution Sampling,” In 23rd Conference on Artificial Intelligence (AAAI), 2008. [PDF]

2007

  • [W 4] Vibhav Gogate, “Approximate Inference in Probabilistic Graphical Models with Determinism,” In Doctoral Program of 22nd Conference on Artificial Intelligence (AAAI), 2007. [PDF]

  • [W 3] Vibhav Gogate and Rina Dechter, “A Simple Application of Sampling Importance Resampling to Solution Sampling,” In Doctoral Program of 13th International Conference on Principles and Practice of Constraint Programming (CP), 2007. [PDF]

  • [C 9] Vibhav Gogate, Bozhena Bidyuk and Rina Dechter, “Studies in Lower Bounding Probability of Evidence using the Markov Inequality,” In 23rd Conference on Uncertainty in Artificial Intelligence (UAI), 2007. [PDF]

  • [C 8] Vibhav Gogate and Rina Dechter, “Approximate Counting by Sampling the Backtrack-free Search Space,” In 22nd Conference on Artificial Intelligence (AAAI), 2007. [PDF]

  • [C 7] Vibhav Gogate and Rina Dechter, “SampleSearch: A Scheme that Searches for Consistent Samples,” In 11th International Conference on Artificial Intelligence and Statistics (AISTATS), 2007. [PDF]

2006

  • [C 6, W 2] Vibhav Gogate and Rina Dechter, “A New Algorithm for Sampling CSP Solutions Uniformly at Random,” In 12th International Conference on Principles and Practice of Constraint Programming (CP), 2006. [PDF]

  • [C 5] Vibhav Gogate, Rina Dechter, Bozhena Bidyuk, James Marca and Craig Rindt, “Modeling Travel and Activity Routines using Hybrid Dynamic Mixed Networks,” In 85th annual meeting of the Transportation Research Board (TRB), 2006. [PDF]

2005

  • [C 4] Vibhav Gogate and Rina Dechter, “Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints,” In 21st Conference on Uncertainty in Artificial Intelligence (UAI), 2005. [PDF]

  • [C 3] Vibhav Gogate, Rina Dechter, Bozhena Bidyuk, James Marca and Craig Rindt, “Modeling Transportation Routines using Hybrid Dynamic Mixed Networks,” In 21st Conference on Uncertainty in Artificial Intelligence (UAI), 2005. [PDF]

2004

  • [C 2] Kalev Kask, Rina Dechter and Vibhav Gogate, “Counting-Based Look-ahead Schemes for Constraint Satisfaction,” In 10th International Conference on Principles and Practice of Constraint Programming (CP), 2004. [PDF]

  • [ W 1 ] Kalev Kask, Rina Dechter and Vibhav Gogate, “New Look-ahead Schemes for Constraint Satisfaction,” In 8th International Symposium on Artificial Intelligence and Mathematics AI+MATH, 2004. [PDF]

  • [ C 1] Vibhav Gogate and Rina Dechter, “A complete Anytime Algorithm for Treewidth,” In 20th Conference on Uncertainty in Artificial Intelligence (UAI), 2004. [PDF]