Vibhav Gogate - RankMe

122 papers A* 26A 33B 1Misc 1Journal 39Unranked 22

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Skill-augmented Agentic Framework and Benchmark for Multi-Video Understanding. CoRR Yue Zhang, Liqiang Jing, Jia Li, Yapeng Tian, Xinya Du, Yunhui Guo, Vibhav Gogate
2026	J	`jnl`	Towards Spatio-Temporal World Scene Graph Generation from Monocular Videos. CoRR Rohith Peddi, Saurabh, Shravan Shanmugam, Likhitha Pallapothula, Yu Xiang, Parag Singla, Vibhav Gogate
2025	J	`jnl`	Can Video Large Multimodal Models Think Like Doubters-or Double-Down: A Study on Defeasible Video Entailment. CoRR Yue Zhang, Jilei Sun, Yunhui Guo, Vibhav Gogate
2025	A*	`conf`	Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven Optimization. AAAI Yue Zhang, Liqiang Jing, Vibhav Gogate
2025	J	`jnl`	Learning to Condition: A Neural Heuristic for Scalable MPE Inference. CoRR Brij Malhotra, Shivvrat Arya, Tahrima Rahman, Vibhav Gogate
2025	A	`conf`	SINE: Scalable MPE Inference for Probabilistic Graphical Models using Advanced Neural Embeddings. AISTATS Shivvrat Arya, Tahrima Rahman, Vibhav Gogate
2025	A*	`conf`	Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation. CVPR Rohith Peddi, Saurabh, Ayush Abhay Shrivastava, Parag Singla, Vibhav Gogate
2024	A*	`conf`	A Neural Network Approach for Efficiently Answering Most Probable Explanation Queries in Probabilistic Models. NeurIPS Shivvrat Arya, Tahrima Rahman, Vibhav Gogate
2024	A*	`conf`	CaptainCook4D: A Dataset for Understanding Errors in Procedural Activities. NeurIPS Rohith Peddi, Shivvrat Arya, Bharath Challa, Likhitha Pallapothula, Akshay Vyas, Bhavya Gouripeddi, Qifan Zhang, Jikai Wang, Vasundhara Komaragiri, Eric D. Ragan, Nicholas Ruozzi, Yu Xiang, Vibhav Gogate
2024	A	`conf`	Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification. AISTATS Shivvrat Arya, Yu Xiang, Vibhav Gogate
2024	J	`jnl`	Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification. CoRR Shivvrat Arya, Yu Xiang, Vibhav Gogate
2024	J	`jnl`	Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven Optimization. CoRR Yue Zhang, Liqiang Jing, Vibhav Gogate
2024	A	`conf`	Grasping Trajectory Optimization with Point Clouds. IROS Yu Xiang, Sai Haneesh Allu, Rohith Peddi, Tyler H. Summers, Vibhav Gogate
2024	J	`jnl`	Grasping Trajectory Optimization with Point Clouds. CoRR Yu Xiang, Sai Haneesh Allu, Rohith Peddi, Tyler H. Summers, Vibhav Gogate
2024	A	`conf`	Learning Distributionally Robust Tractable Probabilistic Models in Continuous Domains. UAI Hailiang Dong, James Amato, Vibhav Gogate, Nicholas Ruozzi
2024	A	`conf`	Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models. AISTATS Shivvrat Arya, Tahrima Rahman, Vibhav Gogate
2024	J	`jnl`	Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models. CoRR Shivvrat Arya, Tahrima Rahman, Vibhav Gogate
2024	A*	`conf`	Neural Network Approximators for Marginal MAP in Probabilistic Circuits. AAAI Shivvrat Arya, Tahrima Rahman, Vibhav Gogate
2024	J	`jnl`	Neural Network Approximators for Marginal MAP in Probabilistic Circuits. CoRR Shivvrat Arya, Tahrima Rahman, Vibhav Gogate
2024	—	`conf`	Predictive Task Guidance with Artificial Intelligence in Augmented Reality. VR Workshops Benjamin Rheault, Shivvrat Arya, Akshay Vyas, Jikai Wang, Rohith Peddi, Brett Benda, Vibhav Gogate, Nicholas Ruozzi, Yu Xiang, Eric D. Ragan
2024	—	`conf`	Towards Scene Graph Anticipation. ECCV (88) Rohith Peddi, Saksham Singh, Saurabh, Parag Singla, Vibhav Gogate
2024	J	`jnl`	Towards Scene Graph Anticipation. CoRR Rohith Peddi, Saksham Singh, Saurabh, Parag Singla, Vibhav Gogate
2024	J	`jnl`	Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation. CoRR Rohith Peddi, Saurabh, Ayush Abhay Shrivastava, Parag Singla, Vibhav Gogate
2023	A	`conf`	A New Modeling Framework for Continuous, Sequential Domains. AISTATS Hailiang Dong, James Amato, Vibhav Gogate, Nicholas Ruozzi
2023	J	`jnl`	CaptainCook4D: A dataset for understanding errors in procedural activities. CoRR 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
2023	J	`jnl`	Deep Dependency Networks for Multi-Label Classification. CoRR Shivvrat Arya, Yu Xiang, Vibhav Gogate
2023	J	`jnl`	Explainable Activity Recognition in Videos using Deep Learning and Tractable Probabilistic Models. ACM Trans. Interact. Intell. Syst. Chiradeep Roy, Mahsan Nourani, Shivvrat Arya, Mahesh Shanbhag, Tahrima Rahman, Eric D. Ragan, Nicholas Ruozzi, Vibhav Gogate
2023	A	`conf`	Knowledge Intensive Learning of Cutset Networks. UAI Saurabh Mathur, Vibhav Gogate, Sriraam Natarajan
2022	A	`conf`	Conditionally Tractable Density Estimation using Neural Networks. AISTATS Hailiang Dong, Chiradeep Roy, Tahrima Rahman, Vibhav Gogate, Nicholas Ruozzi
2022	J	`jnl`	DETOXER: A Visual Debugging Tool With Multiscope Explanations for Temporal Multilabel Classification. IEEE Computer Graphics and Applications Mahsan Nourani, Chiradeep Roy, Donald R. Honeycutt, Eric D. Ragan, Vibhav Gogate
2022	Misc	`conf`	Dissociation-Based Oblivious Bounds for Weighted Model Counting. ISAIM Vibhav Gogate
2022	A*	`conf`	Learning Tractable Probabilistic Models from Inconsistent Local Estimates. NeurIPS Shasha Jin, Vasundhara Komaragiri, Tahrima Rahman, Vibhav Gogate
2022	J	`jnl`	On the Importance of User Backgrounds and Impressions: Lessons Learned from Interactive AI Applications. ACM Trans. Interact. Intell. Syst. Mahsan Nourani, Chiradeep Roy, Jeremy E. Block, Donald R. Honeycutt, Tahrima Rahman, Eric D. Ragan, Vibhav Gogate
2022	A	`conf`	Robust learning of tractable probabilistic models. UAI Rohith Peddi, Tahrima Rahman, Vibhav Gogate
2021	A	`conf`	Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems. IUI Mahsan Nourani, Chiradeep Roy, Jeremy E. Block, Donald R. Honeycutt, Tahrima Rahman, Eric D. Ragan, Vibhav Gogate
2021	A	`conf`	Dynamic Cutset Networks. AISTATS Chiradeep Roy, Tahrima Rahman, Hailiang Dong, Nicholas Ruozzi, Vibhav Gogate
2021	A*	`conf`	Novel Upper Bounds for the Constrained Most Probable Explanation Task. NeurIPS Tahrima Rahman, Sara Rouhani, Vibhav Gogate
2020	A*	`conf`	A Novel Approach for Constrained Optimization in Graphical Models. NeurIPS Sara Rouhani, Tahrima Rahman, Vibhav Gogate
2020	J	`jnl`	Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition. CoRR Mahsan Nourani, Chiradeep Roy, Tahrima Rahman, Eric D. Ragan, Nicholas Ruozzi, Vibhav Gogate
2020	—	`conf`	Investigating the Importance of First Impressions and Explainable AI with Interactive Video Analysis. CHI Extended Abstracts Mahsan Nourani, Donald R. Honeycutt, Jeremy E. Block, Chiradeep Roy, Tahrima Rahman, Eric D. Ragan, Vibhav Gogate
2020	A	`ed.`	Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, UAI 2020, virtual online, August 3-6, 2020 UAI Ryan P. Adams, Vibhav Gogate
2019	A*	`conf`	Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models. IJCAI Tahrima Rahman, Shasha Jin, Vibhav Gogate
2019	A	`conf`	Domain-Size Aware Markov Logic Networks. AISTATS Happy Mittal, Ayush Bhardwaj, Vibhav Gogate, Parag Singla
2019	—	`conf`	Explainable Activity Recognition in Videos. IUI Workshops Chiradeep Roy, Mahesh Shanbhag, Mahsan Nourani, Tahrima Rahman, Samia Kabir, Vibhav Gogate, Nicholas Ruozzi, Eric D. Ragan
2019	A*	`conf`	Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation. ICML Tahrima Rahman, Shasha Jin, Vibhav Gogate
2018	A*	`conf`	Algorithms for the Nearest Assignment Problem. IJCAI Sara Rouhani, Tahrima Rahman, Vibhav Gogate
2018	A*	`conf`	Automatic Parameter Tying: A New Approach for Regularized Parameter Learning in Markov Networks. AAAI Li Chou, Pracheta Sahoo, Somdeb Sarkhel, Nicholas Ruozzi, Vibhav Gogate
2018	A	`conf`	Dissociation-Based Oblivious Bounds for Weighted Model Counting. UAI Li Chou, Wolfgang Gatterbauer, Vibhav Gogate
2018	J	`jnl`	Domain Aware Markov Logic Networks. CoRR Happy Mittal, Ayush Bhardwaj, Vibhav Gogate, Parag Singla
2018	A	`conf`	Lifted Marginal MAP Inference. UAI Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate, Parag Singla
2018	J	`jnl`	Lifted Marginal MAP Inference. CoRR Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate, Parag Singla
2018	J	`jnl`	Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization. CoRR Yibo Yang, Nicholas Ruozzi, Vibhav Gogate
2017	A*	`conf`	Efficient Inference for Untied MLNs. IJCAI Somdeb Sarkhel, Deepak Venugopal, Nicholas Ruozzi, Vibhav Gogate
2017	A*	`conf`	Order Statistics for Probabilistic Graphical Models. IJCAI David B. Smith, Sara Rouhani, Vibhav Gogate
2016	B	`conf`	Joint Inference for Event Coreference Resolution. COLING Jing Lu, Deepak Venugopal, Vibhav Gogate, Vincent Ng
2016	A*	`conf`	Learning Ensembles of Cutset Networks. AAAI Tahrima Rahman, Vibhav Gogate
2016	J	`jnl`	Lifted Region-Based Belief Propagation. CoRR David B. Smith, Parag Singla, Vibhav Gogate
2016	A	`conf`	Merging Strategies for Sum-Product Networks: From Trees to Graphs. UAI Tahrima Rahman, Vibhav Gogate
2016	A*	`conf`	On Parameter Tying by Quantization. AAAI Li Chou, Somdeb Sarkhel, Nicholas Ruozzi, Vibhav Gogate
2016	A*	`conf`	Probabilistic Inference Modulo Theories. IJCAI Rodrigo de Salvo Braz, Ciaran O'Reilly, Vibhav Gogate, Rina Dechter
2016	J	`jnl`	Probabilistic Inference Modulo Theories. CoRR Rodrigo de Salvo Braz, Ciaran O'Reilly, Vibhav Gogate, Rina Dechter
2016	J	`jnl`	Probabilistic theorem proving. Commun. ACM Vibhav Gogate, Pedro M. Domingos
2016	A*	`conf`	Scalable Training of Markov Logic Networks Using Approximate Counting. AAAI Somdeb Sarkhel, Deepak Venugopal, Tuan Anh Pham, Parag Singla, Vibhav Gogate
2015	—	`conf`	Bounding the Cost of Search-Based Lifted Inference. NIPS David B. Smith, Vibhav Gogate
2015	—	`conf`	Fast Lifted MAP Inference via Partitioning. NIPS Somdeb Sarkhel, Parag Singla, Vibhav Gogate
2015	A*	`conf`	Just Count the Satisfied Groundings: Scalable Local-Search and Sampling Based Inference in MLNs. AAAI Deepak Venugopal, Somdeb Sarkhel, Vibhav Gogate
2015	—	`conf`	Lifted Inference Rules With Constraints. NIPS Happy Mittal, Anuj Mahajan, Vibhav Gogate, Parag Singla
2014	—	`conf`	An Integer Polynomial Programming Based Framework for Lifted MAP Inference. NIPS Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav Gogate
2014	—	`conf`	Cutset Networks: A Simple, Tractable, and Scalable Approach for Improving the Accuracy of Chow-Liu Trees. ECML/PKDD (2) Tahrima Rahman, Prasanna V. Kothalkar, Vibhav Gogate
2014	—	`conf`	Evidence-Based Clustering for Scalable Inference in Markov Logic. StarAI@AAAI Deepak Venugopal, Vibhav Gogate
2014	—	`conf`	Evidence-Based Clustering for Scalable Inference in Markov Logic. ECML/PKDD (3) Deepak Venugopal, Vibhav Gogate
2014	J	`jnl`	Join-Graph Propagation Algorithms. CoRR Robert Mateescu, Kalev Kask, Vibhav Gogate, Rina Dechter
2014	A	`conf`	Lifted MAP Inference for Markov Logic Networks. AISTATS Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav Gogate
2014	A	`conf`	Loopy Belief Propagation in the Presence of Determinism. AISTATS David B. Smith, Vibhav Gogate
2014	—	`conf`	New Rules for Domain Independent Lifted MAP Inference. NIPS Happy Mittal, Prasoon Goyal, Vibhav Gogate, Parag Singla
2014	A*	`conf`	Relieving the Computational Bottleneck: Joint Inference for Event Extraction with High-Dimensional Features. EMNLP Deepak Venugopal, Chen Chen, Vibhav Gogate, Vincent Ng
2014	—	`conf`	Scaling-up Importance Sampling for Markov Logic Networks. NIPS Deepak Venugopal, Vibhav Gogate
2013	A	`conf`	Dynamic Blocking and Collapsing for Gibbs Sampling. UAI Deepak Venugopal, Vibhav Gogate
2013	J	`jnl`	Dynamic Blocking and Collapsing for Gibbs Sampling. CoRR Deepak Venugopal, Vibhav Gogate
2013	A*	`conf`	GiSS: Combining Gibbs Sampling and SampleSearch for Inference in Mixed Probabilistic and Deterministic Graphical Models. AAAI Deepak Venugopal, Vibhav Gogate
2013	—	`conf`	Lifting WALKSAT-Based Local Search Algorithms for MAP Inference. StarAI@AAAI Somdeb Sarkhel, Vibhav Gogate
2013	—	`conf`	Organizers. StarAI@AAAI Vibhav Gogate
2013	—	`conf`	Preface. StarAI@AAAI Vibhav Gogate, Kristian Kersting, Sriraam Natarajan, David Poole
2013	A	`conf`	Structured Message Passing. UAI Vibhav Gogate, Pedro M. Domingos
2013	J	`jnl`	Structured Message Passing. CoRR Vibhav Gogate, Pedro M. Domingos
2013	J	`jnl`	The AAAI-13 Conference Workshops. AI Mag. 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
2013	A*	`conf`	The Inclusion-Exclusion Rule and its Application to the Junction Tree Algorithm. IJCAI David B. Smith, Vibhav Gogate
2012	J	`jnl`	A Complete Anytime Algorithm for Treewidth CoRR Vibhav Gogate, Rina Dechter
2012	J	`jnl`	AND/OR Importance Sampling CoRR Vibhav Gogate, Rina Dechter
2012	A*	`conf`	Advances in Lifted Importance Sampling. AAAI Vibhav Gogate, Abhay Kumar Jha, Deepak Venugopal
2012	J	`jnl`	Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints CoRR Vibhav Gogate, Rina Dechter
2012	J	`jnl`	Approximation by Quantization CoRR Vibhav Gogate, Pedro M. Domingos
2012	J	`jnl`	Formula-Based Probabilistic Inference CoRR Vibhav Gogate, Pedro M. Domingos
2012	J	`jnl`	Importance sampling-based estimation over AND/OR search spaces for graphical models. Artif. Intell. Vibhav Gogate, Rina Dechter
2012	J	`jnl`	Modeling Transportation Routines using Hybrid Dynamic Mixed Networks CoRR Vibhav Gogate, Rina Dechter, Bozhena Bidyuk, Craig Rindt, James Marca
2012	—	`conf`	On Lifting the Gibbs Sampling Algorithm. NIPS Deepak Venugopal, Vibhav Gogate
2012	—	`conf`	On Lifting the Gibbs Sampling Algorithm. StarAI@UAI Deepak Venugopal, Vibhav Gogate
2012	J	`jnl`	Probabilistic Theorem Proving CoRR Vibhav Gogate, Pedro M. Domingos
2012	J	`jnl`	Studies in Lower Bounding Probabilities of Evidence using the Markov Inequality CoRR Vibhav Gogate, Bozhena Bidyuk, Rina Dechter
2011	A	`conf`	Approximation by Quantization. UAI Vibhav Gogate, Pedro M. Domingos
2011	A	`conf`	Probabilistic Theorem Proving. UAI Vibhav Gogate, Pedro M. Domingos
2011	J	`jnl`	SampleSearch: Importance sampling in presence of determinism. Artif. Intell. Vibhav Gogate, Rina Dechter
2011	J	`jnl`	Sampling-based lower bounds for counting queries. Intelligenza Artificiale Vibhav Gogate, Rina Dechter
2010	—	`conf`	Exploiting Logical Structure in Lifted Probabilistic Inference. StarAI@AAAI Vibhav Gogate, Pedro M. Domingos
2010	A	`conf`	Formula-Based Probabilistic Inference. UAI Vibhav Gogate, Pedro M. Domingos
2010	J	`jnl`	Join-Graph Propagation Algorithms. J. Artif. Intell. Res. Robert Mateescu, Kalev Kask, Vibhav Gogate, Rina Dechter
2010	—	`conf`	Learning Efficient Markov Networks. NIPS Vibhav Gogate, William Austin Webb, Pedro M. Domingos
2010	—	`conf`	Lifted Inference Seen from the Other Side : The Tractable Features. NIPS Abhay Kumar Jha, Vibhav Gogate, Alexandra Meliou, Dan Suciu
2010	A	`conf`	On Combining Graph-based Variance Reduction schemes. AISTATS Vibhav Gogate, Rina Dechter
2008	A	`conf`	AND/OR Importance Sampling. UAI Vibhav Gogate, Rina Dechter
2008	A	`conf`	Approximate Solution Sampling (and Counting) on AND/OR Spaces. CP Vibhav Gogate, Rina Dechter
2008	A*	`conf`	Studies in Solution Sampling. AAAI Vibhav Gogate, Rina Dechter
2007	A*	`conf`	Approximate Counting by Sampling the Backtrack-free Search Space. AAAI Vibhav Gogate, Rina Dechter
2007	A*	`conf`	Approximate Inference in Probabilistic Graphical Models with Determinism. AAAI Vibhav Gogate
2007	A	`conf`	SampleSearch: A Scheme that Searches for Consistent Samples. AISTATS Vibhav Gogate, Rina Dechter
2007	A	`conf`	Studies in Lower Bounding Probabilities of Evidence using the Markov Inequality. UAI Vibhav Gogate, Bozhena Bidyuk, Rina Dechter
2006	A	`conf`	A New Algorithm for Sampling CSP Solutions Uniformly at Random. CP Vibhav Gogate, Rina Dechter
2005	A	`conf`	Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints. UAI Vibhav Gogate, Rina Dechter
2005	A	`conf`	Modeling Transportation Routines using Hybrid Dynamic Mixed Networks. UAI Vibhav Gogate, Rina Dechter, Bozhena Bidyuk, Craig Rindt, James Marca
2004	A	`conf`	A Complete Anytime Algorithm for Treewidth. UAI Vibhav Gogate, Rina Dechter
2004	A	`conf`	Counting-Based Look-Ahead Schemes for Constraint Satisfaction. CP Kalev Kask, Rina Dechter, Vibhav Gogate
2004	—	`conf`	New Look-Ahead Schemes for Constraint Satisfaction. AI&M Kalev Kask, Rina Dechter, Vibhav Gogate