| 2025 | AAAI | Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven Optimization. | Yue Zhang, Liqiang Jing, Vibhav Gogate |
| 2025 | AISTATS | SINE: Scalable MPE Inference for Probabilistic Graphical Models using Advanced Neural Embeddings. | Shivvrat Arya, Tahrima Rahman, Vibhav Gogate |
| 2025 | CVPR | Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation. | Rohith Peddi, Saurabh, Ayush Abhay Shrivastava, Parag Singla, Vibhav Gogate |
| 2024 | AAAI | Neural Network Approximators for Marginal MAP in Probabilistic Circuits. | Shivvrat Arya, Tahrima Rahman, Vibhav Gogate |
| 2024 | AISTATS | Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models. | Shivvrat Arya, Tahrima Rahman, Vibhav Gogate |
| 2024 | AISTATS | Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification. | Shivvrat Arya, Yu Xiang, Vibhav Gogate |
| 2024 | ECCV | Towards Scene Graph Anticipation. | Rohith Peddi, Saksham Singh, Saurabh, Parag Singla, Vibhav Gogate |
| 2024 | IROS | Grasping Trajectory Optimization with Point Clouds. | Yu Xiang, Sai Haneesh Allu, Rohith Peddi, Tyler H. Summers, Vibhav Gogate |
| 2024 | VR | Predictive Task Guidance with Artificial Intelligence in Augmented Reality. | Benjamin Rheault, Shivvrat Arya, Akshay Vyas, Jikai Wang, Rohith Peddi, Brett Benda, Vibhav Gogate, Nicholas Ruozzi, Yu Xiang, Eric D. Ragan |
| 2024 | UAI | Learning Distributionally Robust Tractable Probabilistic Models in Continuous Domains. | Hailiang Dong, James Amato, Vibhav Gogate, Nicholas Ruozzi |
| 2023 | AISTATS | A New Modeling Framework for Continuous, Sequential Domains. | Hailiang Dong, James Amato, Vibhav Gogate, Nicholas Ruozzi |
| 2023 | UAI | Knowledge Intensive Learning of Cutset Networks. | Saurabh Mathur, Vibhav Gogate, Sriraam Natarajan |
| 2022 | AISTATS | Conditionally Tractable Density Estimation using Neural Networks. | Hailiang Dong, Chiradeep Roy, Tahrima Rahman, Vibhav Gogate, Nicholas Ruozzi |
| 2022 | ISAIM | Dissociation-Based Oblivious Bounds for Weighted Model Counting. | Vibhav Gogate |
| 2022 | UAI | Robust learning of tractable probabilistic models. | Rohith Peddi, Tahrima Rahman, Vibhav Gogate |
| 2021 | AISTATS | Dynamic Cutset Networks. | Chiradeep Roy, Tahrima Rahman, Hailiang Dong, Nicholas Ruozzi, Vibhav Gogate |
| 2021 | IUI | Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems. | Mahsan Nourani, Chiradeep Roy, Jeremy E. Block, Donald R. Honeycutt, Tahrima Rahman, Eric D. Ragan, Vibhav Gogate |
| 2020 | CHI | Investigating the Importance of First Impressions and Explainable AI with Interactive Video Analysis. | Mahsan Nourani, Donald R. Honeycutt, Jeremy E. Block, Chiradeep Roy, Tahrima Rahman, Eric D. Ragan, Vibhav Gogate |
| 2019 | AISTATS | Domain-Size Aware Markov Logic Networks. | Happy Mittal, Ayush Bhardwaj, Vibhav Gogate, Parag Singla |
| 2019 | ICML | Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation. | Tahrima Rahman, Shasha Jin, Vibhav Gogate |
| 2019 | IJCAI | Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models. | Tahrima Rahman, Shasha Jin, Vibhav Gogate |
| 2019 | IUI | Explainable Activity Recognition in Videos. | Chiradeep Roy, Mahesh Shanbhag, Mahsan Nourani, Tahrima Rahman, Samia Kabir, Vibhav Gogate, Nicholas Ruozzi, Eric D. Ragan |
| 2018 | AAAI | Automatic Parameter Tying: A New Approach for Regularized Parameter Learning in Markov Networks. | Li Chou, Pracheta Sahoo, Somdeb Sarkhel, Nicholas Ruozzi, Vibhav Gogate |
| 2018 | IJCAI | Algorithms for the Nearest Assignment Problem. | Sara Rouhani, Tahrima Rahman, Vibhav Gogate |
| 2018 | UAI | Dissociation-Based Oblivious Bounds for Weighted Model Counting. | Li Chou, Wolfgang Gatterbauer, Vibhav Gogate |
| 2018 | UAI | Lifted Marginal MAP Inference. | Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate, Parag Singla |
| 2017 | IJCAI | Order Statistics for Probabilistic Graphical Models. | David B. Smith, Sara Rouhani, Vibhav Gogate |
| 2017 | IJCAI | Efficient Inference for Untied MLNs. | Somdeb Sarkhel, Deepak Venugopal, Nicholas Ruozzi, Vibhav Gogate |
| 2016 | AAAI | On Parameter Tying by Quantization. | Li Chou, Somdeb Sarkhel, Nicholas Ruozzi, Vibhav Gogate |
| 2016 | AAAI | Learning Ensembles of Cutset Networks. | Tahrima Rahman, Vibhav Gogate |
| 2016 | AAAI | Scalable Training of Markov Logic Networks Using Approximate Counting. | Somdeb Sarkhel, Deepak Venugopal, Tuan Anh Pham, Parag Singla, Vibhav Gogate |
| 2016 | COLING | Joint Inference for Event Coreference Resolution. | Jing Lu, Deepak Venugopal, Vibhav Gogate, Vincent Ng |
| 2016 | IJCAI | Probabilistic Inference Modulo Theories. | Rodrigo de Salvo Braz, Ciaran O'Reilly, Vibhav Gogate, Rina Dechter |
| 2016 | UAI | Merging Strategies for Sum-Product Networks: From Trees to Graphs. | Tahrima Rahman, Vibhav Gogate |
| 2015 | AAAI | Just Count the Satisfied Groundings: Scalable Local-Search and Sampling Based Inference in MLNs. | Deepak Venugopal, Somdeb Sarkhel, Vibhav Gogate |
| 2014 | AAAI | Evidence-Based Clustering for Scalable Inference in Markov Logic. | Deepak Venugopal, Vibhav Gogate |
| 2014 | AISTATS | Lifted MAP Inference for Markov Logic Networks. | Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav Gogate |
| 2014 | AISTATS | Loopy Belief Propagation in the Presence of Determinism. | David B. Smith, Vibhav Gogate |
| 2014 | EMNLP | Relieving the Computational Bottleneck: Joint Inference for Event Extraction with High-Dimensional Features. | Deepak Venugopal, Chen Chen, Vibhav Gogate, Vincent Ng |
| 2013 | AAAI | Organizers. | Vibhav Gogate |
| 2013 | AAAI | Preface. | Vibhav Gogate, Kristian Kersting, Sriraam Natarajan, David Poole |
| 2013 | AAAI | Lifting WALKSAT-Based Local Search Algorithms for MAP Inference. | Somdeb Sarkhel, Vibhav Gogate |
| 2013 | AAAI | GiSS: Combining Gibbs Sampling and SampleSearch for Inference in Mixed Probabilistic and Deterministic Graphical Models. | Deepak Venugopal, Vibhav Gogate |
| 2013 | IJCAI | The Inclusion-Exclusion Rule and its Application to the Junction Tree Algorithm. | David B. Smith, Vibhav Gogate |
| 2013 | UAI | Structured Message Passing. | Vibhav Gogate, Pedro M. Domingos |
| 2013 | UAI | Dynamic Blocking and Collapsing for Gibbs Sampling. | Deepak Venugopal, Vibhav Gogate |
| 2012 | AAAI | Advances in Lifted Importance Sampling. | Vibhav Gogate, Abhay Kumar Jha, Deepak Venugopal |
| 2011 | UAI | Approximation by Quantization. | Vibhav Gogate, Pedro M. Domingos |
| 2011 | UAI | Probabilistic Theorem Proving. | Vibhav Gogate, Pedro M. Domingos |
| 2010 | AAAI | Exploiting Logical Structure in Lifted Probabilistic Inference. | Vibhav Gogate, Pedro M. Domingos |
| 2010 | UAI | Formula-Based Probabilistic Inference. | Vibhav Gogate, Pedro M. Domingos |
| 2008 | AAAI | Studies in Solution Sampling. | Vibhav Gogate, Rina Dechter |
| 2008 | CP | Approximate Solution Sampling (and Counting) on AND/OR Spaces. | Vibhav Gogate, Rina Dechter |
| 2008 | UAI | AND/OR Importance Sampling. | Vibhav Gogate, Rina Dechter |
| 2007 | AAAI | Approximate Inference in Probabilistic Graphical Models with Determinism. | Vibhav Gogate |
| 2007 | AAAI | Approximate Counting by Sampling the Backtrack-free Search Space. | Vibhav Gogate, Rina Dechter |
| 2007 | UAI | Studies in Lower Bounding Probabilities of Evidence using the Markov Inequality. | Vibhav Gogate, Bozhena Bidyuk, Rina Dechter |
| 2006 | CP | A New Algorithm for Sampling CSP Solutions Uniformly at Random. | Vibhav Gogate, Rina Dechter |
| 2005 | UAI | Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints. | Vibhav Gogate, Rina Dechter |
| 2005 | UAI | Modeling Transportation Routines using Hybrid Dynamic Mixed Networks. | Vibhav Gogate, Rina Dechter, Bozhena Bidyuk, Craig Rindt, James Marca |
| 2004 | CP | Counting-Based Look-Ahead Schemes for Constraint Satisfaction. | Kalev Kask, Rina Dechter, Vibhav Gogate |
| 2004 | ISAIM | New Look-Ahead Schemes for Constraint Satisfaction. | Kalev Kask, Rina Dechter, Vibhav Gogate |
| 2004 | UAI | A Complete Anytime Algorithm for Treewidth. | Vibhav Gogate, Rina Dechter |