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Vibhav Gogate
Person information
- affiliation: University of Texas at Dallas, Department of Computer Science
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2020 – today
- 2024
- [c74]Shivvrat Arya, Tahrima Rahman, Vibhav Gogate
:
Neural Network Approximators for Marginal MAP in Probabilistic Circuits. AAAI 2024: 10918-10926 - [c73]Shivvrat Arya, Tahrima Rahman, Vibhav Gogate:
Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models. AISTATS 2024: 2791-2799 - [c72]Shivvrat Arya, Yu Xiang, Vibhav Gogate:
Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification. AISTATS 2024: 2818-2826 - [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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