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ORCIDVibhav Gogate
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- affiliation: University of Texas at Dallas, Department of Computer Science
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2020 – today
- 2024
[c75]Shivvrat Arya
, Tahrima Rahman, Vibhav Gogate:
Neural Network Approximators for Marginal MAP in Probabilistic Circuits. AAAI 2024: 10918-10926- [c74]Shivvrat Arya, Tahrima Rahman, Vibhav Gogate:
Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models. AISTATS 2024: 2791-2799 - [c73]Shivvrat Arya, Yu Xiang, Vibhav Gogate:
Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification. AISTATS 2024: 2818-2826 - [c72]Rohith Peddi, Saksham Singh, Saurabh, Parag Singla, Vibhav Gogate:
Towards Scene Graph Anticipation. ECCV (88) 2024: 159-175 - [c71]Benjamin Rheault, Shivvrat Arya, Akshay Vyas, Jikai Wang, Rohith Peddi, Brett Benda, Vibhav Gogate, Nicholas Ruozzi, Yu Xiang, Eric D. Ragan:
Predictive Task Guidance with Artificial Intelligence in Augmented Reality. VR Workshops 2024: 973-974 - [i24]Shivvrat Arya, Tahrima Rahman, Vibhav Gogate:
Neural Network Approximators for Marginal MAP in Probabilistic Circuits. CoRR abs/2402.03621 (2024) - [i23]Rohith Peddi, Saksham Singh, Saurabh, Parag Singla, Vibhav Gogate:
Towards Scene Graph Anticipation. CoRR abs/2403.04899 (2024) - [i22]Yu Xiang, Sai Haneesh Allu, Rohith Peddi, Tyler Summers, Vibhav Gogate:
Grasping Trajectory Optimization with Point Clouds. CoRR abs/2403.05466 (2024) - [i21]Shivvrat Arya, Tahrima Rahman, Vibhav Gogate:
Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models. CoRR abs/2404.11606 (2024) - [i20]Shivvrat Arya, Yu Xiang, Vibhav Gogate:
Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification. CoRR abs/2404.11667 (2024) - 2023
- [j9]Chiradeep Roy, Mahsan Nourani, Shivvrat Arya, Mahesh Shanbhag, Tahrima Rahman, Eric D. Ragan, Nicholas Ruozzi, Vibhav Gogate:
Explainable Activity Recognition in Videos using Deep Learning and Tractable Probabilistic Models. ACM Trans. Interact. Intell. Syst. 13(4): 29:1-29:32 (2023) - [c70]Hailiang Dong, James Amato, Vibhav Gogate, Nicholas Ruozzi:
A New Modeling Framework for Continuous, Sequential Domains. AISTATS 2023: 11118-11131 - [c69]Saurabh Mathur, Vibhav Gogate, Sriraam Natarajan:
Knowledge Intensive Learning of Cutset Networks. UAI 2023: 1380-1389 - [i19]Shivvrat Arya, Yu Xiang, Vibhav Gogate:
Deep Dependency Networks for Multi-Label Classification. CoRR abs/2302.00633 (2023) - [i18]Rohith Peddi, Shivvrat Arya, Bharath Challa, Likhitha Pallapothula, Akshay Vyas, Jikai Wang, Qifan Zhang, Vasundhara Komaragiri, Eric D. Ragan, Nicholas Ruozzi, Yu Xiang, Vibhav Gogate:
CaptainCook4D: A dataset for understanding errors in procedural activities. CoRR abs/2312.14556 (2023) - 2022
- [j8]Mahsan Nourani, Chiradeep Roy, Donald R. Honeycutt, Eric D. Ragan, Vibhav Gogate:
DETOXER: A Visual Debugging Tool With Multiscope Explanations for Temporal Multilabel Classification. IEEE Computer Graphics and Applications 42(6): 37-46 (2022) - [j7]Mahsan Nourani, Chiradeep Roy, Jeremy E. Block, Donald R. Honeycutt, Tahrima Rahman, Eric D. Ragan, Vibhav Gogate:
On the Importance of User Backgrounds and Impressions: Lessons Learned from Interactive AI Applications. ACM Trans. Interact. Intell. Syst. 12(4): 28:1-28:29 (2022) - [c68]Hailiang Dong, Chiradeep Roy, Tahrima Rahman, Vibhav Gogate, Nicholas Ruozzi:
Conditionally Tractable Density Estimation using Neural Networks. AISTATS 2022: 6933-6946 - [c67]Vibhav Gogate:
Dissociation-Based Oblivious Bounds for Weighted Model Counting. ISAIM 2022 - [c66]Shasha Jin, Vasundhara Komaragiri, Tahrima Rahman, Vibhav Gogate:
Learning Tractable Probabilistic Models from Inconsistent Local Estimates. NeurIPS 2022 - [c65]Rohith Peddi, Tahrima Rahman, Vibhav Gogate:
Robust learning of tractable probabilistic models. UAI 2022: 1572-1581 - 2021
- [c64]Chiradeep Roy, Tahrima Rahman, Hailiang Dong, Nicholas Ruozzi, Vibhav Gogate:
Dynamic Cutset Networks. AISTATS 2021: 3106-3114 - [c63]Mahsan Nourani, Chiradeep Roy, Jeremy E. Block, Donald R. Honeycutt, Tahrima Rahman, Eric D. Ragan, Vibhav Gogate:
Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems. IUI 2021: 340-350 - [c62]Tahrima Rahman, Sara Rouhani, Vibhav Gogate:
Novel Upper Bounds for the Constrained Most Probable Explanation Task. NeurIPS 2021: 9613-9624 - 2020
- [c61]Mahsan Nourani, Donald R. Honeycutt, Jeremy E. Block, Chiradeep Roy, Tahrima Rahman, Eric D. Ragan, Vibhav Gogate:
Investigating the Importance of First Impressions and Explainable AI with Interactive Video Analysis. CHI Extended Abstracts 2020: 1-8 - [c60]Sara Rouhani, Tahrima Rahman, Vibhav Gogate:
A Novel Approach for Constrained Optimization in Graphical Models. NeurIPS 2020 - [e1]Ryan P. Adams, Vibhav Gogate:
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, UAI 2020, virtual online, August 3-6, 2020. Proceedings of Machine Learning Research 124, AUAI Press 2020 [contents] - [i17]Mahsan Nourani, Chiradeep Roy, Tahrima Rahman, Eric D. Ragan, Nicholas Ruozzi, Vibhav Gogate:
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition. CoRR abs/2005.02335 (2020)
2010 – 2019
- 2019
- [c59]Happy Mittal, Ayush Bhardwaj, Vibhav Gogate, Parag Singla:
Domain-Size Aware Markov Logic Networks. AISTATS 2019: 3216-3224 - [c58]Tahrima Rahman, Shasha Jin, Vibhav Gogate:
Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation. ICML 2019: 5311-5320 - [c57]Tahrima Rahman, Shasha Jin, Vibhav Gogate:
Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models. IJCAI 2019: 5751-5757 - [c56]Chiradeep Roy, Mahesh Shanbhag, Mahsan Nourani, Tahrima Rahman, Samia Kabir, Vibhav Gogate, Nicholas Ruozzi, Eric D. Ragan:
Explainable Activity Recognition in Videos. IUI Workshops 2019 - 2018
- [c55]Li Chou, Pracheta Sahoo, Somdeb Sarkhel, Nicholas Ruozzi, Vibhav Gogate:
Automatic Parameter Tying: A New Approach for Regularized Parameter Learning in Markov Networks. AAAI 2018: 2860-2867 - [c54]Sara Rouhani, Tahrima Rahman, Vibhav Gogate:
Algorithms for the Nearest Assignment Problem. IJCAI 2018: 5096-5102 - [c53]Li Chou, Wolfgang Gatterbauer, Vibhav Gogate:
Dissociation-Based Oblivious Bounds for Weighted Model Counting. UAI 2018: 866-875 - [c52]Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate, Parag Singla:
Lifted Marginal MAP Inference. UAI 2018: 917-926 - [i16]Yibo Yang, Nicholas Ruozzi, Vibhav Gogate:
Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization. CoRR abs/1806.05355 (2018) - [i15]Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate, Parag Singla:
Lifted Marginal MAP Inference. CoRR abs/1807.00589 (2018) - [i14]Happy Mittal, Ayush Bhardwaj, Vibhav Gogate, Parag Singla:
Domain Aware Markov Logic Networks. CoRR abs/1807.01082 (2018) - 2017
- [c51]Somdeb Sarkhel, Deepak Venugopal, Nicholas Ruozzi, Vibhav Gogate:
Efficient Inference for Untied MLNs. IJCAI 2017: 4617-4624 - [c50]David B. Smith, Sara Rouhani, Vibhav Gogate:
Order Statistics for Probabilistic Graphical Models. IJCAI 2017: 4625-4631 - 2016
- [j6]Vibhav Gogate, Pedro M. Domingos:
Probabilistic theorem proving. Commun. ACM 59(7): 107-115 (2016) - [c49]Somdeb Sarkhel, Deepak Venugopal, Tuan Anh Pham, Parag Singla, Vibhav Gogate:
Scalable Training of Markov Logic Networks Using Approximate Counting. AAAI 2016: 1067-1073 - [c48]Li Chou, Somdeb Sarkhel, Nicholas Ruozzi, Vibhav Gogate:
On Parameter Tying by Quantization. AAAI 2016: 3241-3247 - [c47]Tahrima Rahman, Vibhav Gogate:
Learning Ensembles of Cutset Networks. AAAI 2016: 3301-3307 - [c46]Jing Lu, Deepak Venugopal, Vibhav Gogate, Vincent Ng:
Joint Inference for Event Coreference Resolution. COLING 2016: 3264-3275 - [c45]Rodrigo de Salvo Braz, Ciaran O'Reilly, Vibhav Gogate, Rina Dechter:
Probabilistic Inference Modulo Theories. IJCAI 2016: 3591-3599 - [c44]Tahrima Rahman, Vibhav Gogate:
Merging Strategies for Sum-Product Networks: From Trees to Graphs. UAI 2016 - [i13]Rodrigo de Salvo Braz, Ciaran O'Reilly, Vibhav Gogate, Rina Dechter:
Probabilistic Inference Modulo Theories. CoRR abs/1605.08367 (2016) - [i12]David B. Smith, Parag Singla, Vibhav Gogate:
Lifted Region-Based Belief Propagation. CoRR abs/1606.09637 (2016) - 2015
- [c43]Deepak Venugopal, Somdeb Sarkhel, Vibhav Gogate:
Just Count the Satisfied Groundings: Scalable Local-Search and Sampling Based Inference in MLNs. AAAI 2015: 3606-3612 - [c42]David B. Smith, Vibhav Gogate:
Bounding the Cost of Search-Based Lifted Inference. NIPS 2015: 946-954 - [c41]Somdeb Sarkhel, Parag Singla, Vibhav Gogate:
Fast Lifted MAP Inference via Partitioning. NIPS 2015: 3240-3248 - [c40]Happy Mittal, Anuj Mahajan, Vibhav Gogate, Parag Singla:
Lifted Inference Rules With Constraints. NIPS 2015: 3519-3527 - 2014
- [c39]Deepak Venugopal, Vibhav Gogate:
Evidence-Based Clustering for Scalable Inference in Markov Logic. StarAI@AAAI 2014 - [c38]Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav Gogate:
Lifted MAP Inference for Markov Logic Networks. AISTATS 2014: 859-867 - [c37]David B. Smith, Vibhav Gogate:
Loopy Belief Propagation in the Presence of Determinism. AISTATS 2014: 895-903 - [c36]Deepak Venugopal, Chen Chen, Vibhav Gogate, Vincent Ng:
Relieving the Computational Bottleneck: Joint Inference for Event Extraction with High-Dimensional Features. EMNLP 2014: 831-843 - [c35]Happy Mittal, Prasoon Goyal, Vibhav Gogate, Parag Singla:
New Rules for Domain Independent Lifted MAP Inference. NIPS 2014: 649-657 - [c34]Deepak Venugopal, Vibhav Gogate:
Scaling-up Importance Sampling for Markov Logic Networks. NIPS 2014: 2978-2986 - [c33]Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav Gogate:
An Integer Polynomial Programming Based Framework for Lifted MAP Inference. NIPS 2014: 3302-3310 - [c32]Deepak Venugopal, Vibhav Gogate:
Evidence-Based Clustering for Scalable Inference in Markov Logic. ECML/PKDD (3) 2014: 258-273 - [c31]Tahrima Rahman, Prasanna V. Kothalkar, Vibhav Gogate:
Cutset Networks: A Simple, Tractable, and Scalable Approach for Improving the Accuracy of Chow-Liu Trees. ECML/PKDD (2) 2014: 630-645 - [i11]Robert Mateescu, Kalev Kask, Vibhav Gogate, Rina Dechter:
Join-Graph Propagation Algorithms. CoRR abs/1401.3489 (2014) - 2013
- [j5]Vikas Agrawal, Christopher Archibald, Mehul Bhatt, Hung Bui, Diane J. Cook, Juan Cortés, Christopher W. Geib, Vibhav Gogate, Hans W. Guesgen, Dietmar Jannach, Michael Johanson, Kristian Kersting, George Dimitri Konidaris, Lars Kotthoff, Martin Michalowski, Sriraam Natarajan, Barry O'Sullivan, Marc Pickett, Vedran Podobnik, David Poole, Lokendra Shastri, Amarda Shehu, Gita Sukthankar:
The AAAI-13 Conference Workshops. AI Mag. 34(4): 9- (2013) - [c30]Vibhav Gogate:
Organizers. StarAI@AAAI 2013 - [c29]Vibhav Gogate, Kristian Kersting, Sriraam Natarajan, David Poole:
Preface. StarAI@AAAI 2013 - [c28]Somdeb Sarkhel, Vibhav Gogate:
Lifting WALKSAT-Based Local Search Algorithms for MAP Inference. StarAI@AAAI 2013 - [c27]Deepak Venugopal, Vibhav Gogate:
GiSS: Combining Gibbs Sampling and SampleSearch for Inference in Mixed Probabilistic and Deterministic Graphical Models. AAAI 2013: 897-904 - [c26]David B. Smith, Vibhav Gogate:
The Inclusion-Exclusion Rule and its Application to the Junction Tree Algorithm. IJCAI 2013: 2568-2575 - [c25]Vibhav Gogate, Pedro M. Domingos:
Structured Message Passing. UAI 2013 - [c24]Deepak Venugopal, Vibhav Gogate:
Dynamic Blocking and Collapsing for Gibbs Sampling. UAI 2013 - [i10]Vibhav Gogate, Pedro M. Domingos:
Structured Message Passing. CoRR abs/1309.6832 (2013) - [i9]Deepak Venugopal, Vibhav Gogate:
Dynamic Blocking and Collapsing for Gibbs Sampling. CoRR abs/1309.6870 (2013) - 2012
- [j4]Vibhav Gogate, Rina Dechter:
Importance sampling-based estimation over AND/OR search spaces for graphical models. Artif. Intell. 184-185: 38-77 (2012) - [c23]Vibhav Gogate, Abhay Kumar Jha, Deepak Venugopal:
Advances in Lifted Importance Sampling. AAAI 2012: 1910-1916 - [c22]Deepak Venugopal, Vibhav Gogate:
On Lifting the Gibbs Sampling Algorithm. NIPS 2012: 1664-1672 - [c21]Deepak Venugopal, Vibhav Gogate:
On Lifting the Gibbs Sampling Algorithm. StarAI@UAI 2012 - [i8]Vibhav Gogate, Pedro M. Domingos:
Approximation by Quantization. CoRR abs/1202.3723 (2012) - [i7]Vibhav Gogate, Pedro M. Domingos:
Probabilistic Theorem Proving. CoRR abs/1202.3724 (2012) - [i6]Vibhav Gogate, Pedro M. Domingos:
Formula-Based Probabilistic Inference. CoRR abs/1203.3482 (2012) - [i5]Vibhav Gogate, Rina Dechter:
AND/OR Importance Sampling. CoRR abs/1206.3232 (2012) - [i4]Vibhav Gogate, Bozhena Bidyuk, Rina Dechter:
Studies in Lower Bounding Probabilities of Evidence using the Markov Inequality. CoRR abs/1206.5242 (2012) - [i3]Vibhav Gogate, Rina Dechter, Bozhena Bidyuk, Craig Rindt, James Marca:
Modeling Transportation Routines using Hybrid Dynamic Mixed Networks. CoRR abs/1207.1384 (2012) - [i2]Vibhav Gogate, Rina Dechter:
Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints. CoRR abs/1207.1385 (2012) - [i1]Vibhav Gogate, Rina Dechter:
A Complete Anytime Algorithm for Treewidth. CoRR abs/1207.4109 (2012) - 2011
- [j3]Vibhav Gogate, Rina Dechter:
SampleSearch: Importance sampling in presence of determinism. Artif. Intell. 175(2): 694-729 (2011) - [j2]Vibhav Gogate, Rina Dechter:
Sampling-based lower bounds for counting queries. Intelligenza Artificiale 5(2): 171-188 (2011) - [c20]Vibhav Gogate, Pedro M. Domingos:
Approximation by Quantization. UAI 2011: 247-255 - [c19]Vibhav Gogate, Pedro M. Domingos:
Probabilistic Theorem Proving. UAI 2011: 256-265 - 2010
- [j1]Robert Mateescu, Kalev Kask, Vibhav Gogate, Rina Dechter:
Join-Graph Propagation Algorithms. J. Artif. Intell. Res. 37: 279-328 (2010) - [c18]Vibhav Gogate, Pedro M. Domingos:
Exploiting Logical Structure in Lifted Probabilistic Inference. StarAI@AAAI 2010 - [c17]Vibhav Gogate, William Austin Webb, Pedro M. Domingos:
Learning Efficient Markov Networks. NIPS 2010: 748-756 - [c16]Abhay Kumar Jha, Vibhav Gogate, Alexandra Meliou, Dan Suciu:
Lifted Inference Seen from the Other Side : The Tractable Features. NIPS 2010: 973-981 - [c15]Vibhav Gogate, Pedro M. Domingos:
Formula-Based Probabilistic Inference. UAI 2010: 210-219 - [c14]Vibhav Gogate, Rina Dechter:
On Combining Graph-based Variance Reduction schemes. AISTATS 2010: 257-264
2000 – 2009
- 2008
- [c13]Vibhav Gogate, Rina Dechter:
Studies in Solution Sampling. AAAI 2008: 271-276 - [c12]Vibhav Gogate, Rina Dechter:
Approximate Solution Sampling (and Counting) on AND/OR Spaces. CP 2008: 534-538 - [c11]Vibhav Gogate, Rina Dechter:
AND/OR Importance Sampling. UAI 2008: 212-219 - 2007
- [c10]Vibhav Gogate, Rina Dechter:
Approximate Counting by Sampling the Backtrack-free Search Space. AAAI 2007: 198-203 - [c9]Vibhav Gogate:
Approximate Inference in Probabilistic Graphical Models with Determinism. AAAI 2007: 1927-1928 - [c8]Vibhav Gogate, Bozhena Bidyuk, Rina Dechter:
Studies in Lower Bounding Probabilities of Evidence using the Markov Inequality. UAI 2007: 141-148 - [c7]Vibhav Gogate, Rina Dechter:
SampleSearch: A Scheme that Searches for Consistent Samples. AISTATS 2007: 147-154 - 2006
- [c6]Vibhav Gogate, Rina Dechter:
A New Algorithm for Sampling CSP Solutions Uniformly at Random. CP 2006: 711-715 - 2005
- [c5]Vibhav Gogate, Rina Dechter:
Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints. UAI 2005: 209-216 - [c4]Vibhav Gogate, Rina Dechter, Bozhena Bidyuk, Craig Rindt, James Marca:
Modeling Transportation Routines using Hybrid Dynamic Mixed Networks. UAI 2005: 217-224 - 2004
- [c3]Kalev Kask, Rina Dechter, Vibhav Gogate:
Counting-Based Look-Ahead Schemes for Constraint Satisfaction. CP 2004: 317-331 - [c2]Kalev Kask, Rina Dechter, Vibhav Gogate:
New Look-Ahead Schemes for Constraint Satisfaction. AI&M 2004 - [c1]Vibhav Gogate, Rina Dechter:
A Complete Anytime Algorithm for Treewidth. UAI 2004: 201-208
Coauthor Index
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