Gogate, Vibhav

59 publications

AAAI 2025 Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven Optimization Yue Zhang, Liqiang Jing, Vibhav Gogate

CVPR 2025 Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Rohith Peddi, Saurabh Saurabh, Ayush Abhay Shrivastava, Parag Singla, Vibhav Gogate

NeurIPS 2024 A Neural Network Approach for Efficiently Answering Most Probable Explanation Queries in Probabilistic Models Shivvrat Arya, Tahrima Rahman, Vibhav Gogate

NeurIPS 2024 CaptainCook4D: A Dataset for Understanding Errors in Procedural Activities Rohith Peddi, Shivvrat Arya, Bharath Challa, Likhitha Pallapothula, Akshay Vyas, Bhavya Gouripeddi, Qifan Zhang, Jikai Wang, Vasundhara Komaragiri, Eric Ragan, Nicholas Ruozzi, Yu Xiang, Vibhav Gogate

AISTATS 2024 Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification Shivvrat Arya, Yu Xiang, Vibhav Gogate

UAI 2024 Learning Distributionally Robust Tractable Probabilistic Models in Continuous Domains Hailiang Dong, James Amato, Vibhav Gogate, Nicholas Ruozzi

AISTATS 2024 Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models Shivvrat Arya, Tahrima Rahman, Vibhav Gogate

AAAI 2024 Neural Network Approximators for Marginal MAP in Probabilistic Circuits Shivvrat Arya, Tahrima Rahman, Vibhav Gogate

ECCV 2024 Towards Scene Graph Anticipation Rohith Peddi, Saksham Singh, Saurabh, Parag Singla, Vibhav Gogate

AISTATS 2023 A New Modeling Framework for Continuous, Sequential Domains Hailiang Dong, James Amato, Vibhav Gogate, Nicholas Ruozzi

UAI 2023 Knowledge Intensive Learning of Cutset Networks Saurabh Mathur, Vibhav Gogate, Sriraam Natarajan

AISTATS 2022 Conditionally Tractable Density Estimation Using Neural Networks Hailiang Dong, Chiradeep Roy, Tahrima Rahman, Vibhav Gogate, Nicholas Ruozzi

NeurIPS 2022 Learning Tractable Probabilistic Models from Inconsistent Local Estimates Shasha Jin, Vasundhara Komaragiri, Tahrima Rahman, Vibhav Gogate

UAI 2022 Robust Learning of Tractable Probabilistic Models Rohith Peddi, Tahrima Rahman, Vibhav Gogate

AISTATS 2021 Dynamic Cutset Networks Chiradeep Roy, Tahrima Rahman, Hailiang Dong, Nicholas Ruozzi, Vibhav Gogate

NeurIPS 2021 Novel Upper Bounds for the Constrained Most Probable Explanation Task Tahrima Rahman, Sara Rouhani, Vibhav Gogate

NeurIPS 2020 A Novel Approach for Constrained Optimization in Graphical Models Sara Rouhani, Tahrima Rahman, Vibhav Gogate

IJCAI 2019 Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models Tahrima Rahman, Shasha Jin, Vibhav Gogate

AISTATS 2019 Domain-Size Aware Markov Logic Networks Happy Mittal, Ayush Bhardwaj, Vibhav Gogate, Parag Singla

ICML 2019 Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation Tahrima Rahman, Shasha Jin, Vibhav Gogate

UAI 2019 The 35th Uncertainty in Artificial Intelligence Conference: Preface Ryan Adams, Vibhav Gogate

IJCAI 2018 Algorithms for the Nearest Assignment Problem Sara Rouhani, Tahrima Rahman, Vibhav Gogate

AAAI 2018 Automatic Parameter Tying: A New Approach for Regularized Parameter Learning in Markov Networks Li Chou, Pracheta Sahoo, Somdeb Sarkhel, Nicholas Ruozzi, Vibhav Gogate

UAI 2018 Dissociation-Based Oblivious Bounds for Weighted Model Counting Li Chou, Wolfgang Gatterbauer, Vibhav Gogate

UAI 2018 Lifted Marginal MAP Inference Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate, Parag Singla

IJCAI 2017 Efficient Inference for Untied MLNs Somdeb Sarkhel, Deepak Venugopal, Nicholas Ruozzi, Vibhav Gogate

IJCAI 2017 Order Statistics for Probabilistic Graphical Models David B. Smith, Sara Rouhani, Vibhav Gogate

AAAI 2016 Learning Ensembles of Cutset Networks Tahrima Rahman, Vibhav Gogate

UAI 2016 Merging Strategies for Sum-Product Networks: From Trees to Graphs Tahrima Rahman, Vibhav Gogate

AAAI 2016 On Parameter Tying by Quantization Li Chou, Somdeb Sarkhel, Nicholas Ruozzi, Vibhav Gogate

IJCAI 2016 Probabilistic Inference Modulo Theories Rodrigo de Salvo Braz, Ciaran O'Reilly, Vibhav Gogate, Rina Dechter

AAAI 2016 Scalable Training of Markov Logic Networks Using Approximate Counting Somdeb Sarkhel, Deepak Venugopal, Tuan Anh Pham, Parag Singla, Vibhav Gogate

AAAI 2015 Just Count the Satisfied Groundings: Scalable Local-Search and Sampling Based Inference in MLNs Deepak Venugopal, Somdeb Sarkhel, Vibhav Gogate

ECML-PKDD 2014 Cutset Networks: A Simple, Tractable, and Scalable Approach for Improving the Accuracy of Chow-Liu Trees Tahrima Rahman, Prasanna V. Kothalkar, Vibhav Gogate

ECML-PKDD 2014 Evidence-Based Clustering for Scalable Inference in Markov Logic Deepak Venugopal, Vibhav Gogate

AISTATS 2014 Lifted MAP Inference for Markov Logic Networks Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav Gogate

AISTATS 2014 Loopy Belief Propagation in the Presence of Determinism David B. Smith, Vibhav Gogate

UAI 2013 Dynamic Blocking and Collapsing for Gibbs Sampling Deepak Venugopal, Vibhav Gogate

AAAI 2013 GiSS: Combining Gibbs Sampling and SampleSearch for Inference in Mixed Probabilistic and Deterministic Graphical Models Deepak Venugopal, Vibhav Gogate

UAI 2013 Structured Message Passing Vibhav Gogate, Pedro M. Domingos

IJCAI 2013 The Inclusion-Exclusion Rule and Its Application to the Junction Tree Algorithm David B. Smith, Vibhav Gogate

AAAI 2012 Advances in Lifted Importance Sampling Vibhav Gogate, Abhay Kumar Jha, Deepak Venugopal

NeurIPS 2012 On Lifting the Gibbs Sampling Algorithm Deepak Venugopal, Vibhav Gogate

UAI 2011 Approximation by Quantization Vibhav Gogate, Pedro M. Domingos

UAI 2011 Probabilistic Theorem Proving Vibhav Gogate, Pedro M. Domingos

UAI 2010 Formula-Based Probabilistic Inference Vibhav Gogate, Pedro M. Domingos

JAIR 2010 Join-Graph Propagation Algorithms Robert Mateescu, Kalev Kask, Vibhav Gogate, Rina Dechter

NeurIPS 2010 Learning Efficient Markov Networks Vibhav Gogate, William Webb, Pedro Domingos

NeurIPS 2010 Lifted Inference Seen from the Other Side : The Tractable Features Abhay Jha, Vibhav Gogate, Alexandra Meliou, Dan Suciu

AISTATS 2010 On Combining Graph-Based Variance Reduction Schemes Vibhav Gogate, Rina Dechter

UAI 2008 AND/OR Importance Sampling Vibhav Gogate, Rina Dechter

AAAI 2008 Studies in Solution Sampling Vibhav Gogate, Rina Dechter

AAAI 2007 Approximate Counting by Sampling the Backtrack-Free Search Space Vibhav Gogate, Rina Dechter

AAAI 2007 Approximate Inference in Probabilistic Graphical Models with Determinism Vibhav Gogate

AISTATS 2007 SampleSearch: A Scheme That Searches for Consistent Samples Vibhav Gogate, Rina Dechter

UAI 2007 Studies in Lower Bounding Probabilities of Evidence Using the Markov Inequality Vibhav Gogate, Bozhena Bidyuk, Rina Dechter

UAI 2005 Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints Vibhav Gogate, Rina Dechter

UAI 2005 Modeling Transportation Routines Using Hybrid Dynamic Mixed Networks Vibhav Gogate, Rina Dechter, Bozhena Bidyuk, Craig Rindt, James Marca

UAI 2004 A Complete Anytime Algorithm for Treewidth Vibhav Gogate, Rina Dechter