Counting-Based Look-Ahead Schemes for Constraint Satisfaction. - Kalev Kask; Rina Dechter; Vibhav Gogate (CP, 2004)

New Look-Ahead Schemes for Constraint Satisfaction. - Kalev Kask; Rina Dechter; Vibhav Gogate (AI&M, 2004)

A Complete Anytime Algorithm for Treewidth. - Vibhav Gogate; Rina Dechter (UAI, 2004)

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, 2005)

A New Algorithm for Sampling CSP Solutions Uniformly at Random. - Vibhav Gogate; Rina Dechter (CP, 2006)

Approximate Inference in Probabilistic Graphical Models with Determinism. - Vibhav Gogate (AAAI, 2007)

Approximate Counting by Sampling the Backtrack-free Search Space. - Vibhav Gogate; Rina Dechter (AAAI, 2007)

Studies in Lower Bounding Probabilities of Evidence using the Markov Inequality. - Vibhav Gogate; Bozhena Bidyuk; Rina Dechter (UAI, 2007)

SampleSearch: A Scheme that Searches for Consistent Samples. - Vibhav Gogate; Rina Dechter (AISTATS, 2007)

Studies in Solution Sampling. - Vibhav Gogate; Rina Dechter (AAAI, 2008)

Approximate Solution Sampling (and Counting) on AND/OR Spaces. - Vibhav Gogate; Rina Dechter (CP, 2008)

AND/OR Importance Sampling. - Vibhav Gogate; Rina Dechter (UAI, 2008)

Exploiting Logical Structure in Lifted Probabilistic Inference. - Vibhav Gogate; Pedro M. Domingos (StarAI@AAAI, 2010)

Learning Efficient Markov Networks. - Vibhav Gogate; William Austin Webb; Pedro M. Domingos (NIPS, 2010)

Lifted Inference Seen from the Other Side : The Tractable Features. - Abhay Kumar Jha; Vibhav Gogate; Alexandra Meliou; Dan Suciu (NIPS, 2010)

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

Join-Graph Propagation Algorithms. - Robert Mateescu; Kalev Kask; Vibhav Gogate; Rina Dechter (J. Artif. Intell. Res., 2010)

On Combining Graph-based Variance Reduction schemes. - Vibhav Gogate; Rina Dechter (AISTATS, 2010)

Approximation by Quantization. - Vibhav Gogate; Pedro M. Domingos (UAI, 2011)

SampleSearch: Importance sampling in presence of determinism. - Vibhav Gogate; Rina Dechter (Artif. Intell., 2011)

Sampling-based lower bounds for counting queries. - Vibhav Gogate; Rina Dechter (Intelligenza Artificiale, 2011)

Advances in Lifted Importance Sampling. - Vibhav Gogate; Abhay Kumar Jha; Deepak Venugopal (AAAI, 2012)

On Lifting the Gibbs Sampling Algorithm. - Deepak Venugopal; Vibhav Gogate (NIPS, 2012)

Importance sampling-based estimation over AND/OR search spaces for graphical models. - Vibhav Gogate; Rina Dechter (Artif. Intell., 2012)

Organizers. - Vibhav Gogate (StarAI@AAAI, 2013)

Preface. - Vibhav Gogate; Kristian Kersting; Sriraam Natarajan; David Poole 001 (StarAI@AAAI, 2013)

Lifting WALKSAT-Based Local Search Algorithms for MAP Inference. - Somdeb Sarkhel; Vibhav Gogate (StarAI@AAAI, 2013)

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

The Inclusion-Exclusion Rule and its Application to the Junction Tree Algorithm. - David B. Smith 002; Vibhav Gogate (IJCAI, 2013)

Structured Message Passing. - Vibhav Gogate; Pedro M. Domingos (UAI, 2013)

Dynamic Blocking and Collapsing for Gibbs Sampling. - Deepak Venugopal; Vibhav Gogate (UAI, 2013)

The AAAI-13 Conference Workshops. - Vikas Agrawal; Christopher Archibald; Mehul Bhatt; Hung Bui 001; Diane J. Cook; Juan Cortés; Christopher W. Geib; Vibhav Gogate; Hans W. Guesgen; Dietmar Jannach; Michael Johanson; Kristian Kersting; et al. (AI Mag., 2013)

Evidence-Based Clustering for Scalable Inference in Markov Logic. - Deepak Venugopal; Vibhav Gogate (StarAI@AAAI, 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 002; Vibhav Gogate (AISTATS, 2014)

Relieving the Computational Bottleneck: Joint Inference for Event Extraction with High-Dimensional Features. - Deepak Venugopal; Chen Chen 004; Vibhav Gogate; Vincent Ng 001 (EMNLP, 2014)

New Rules for Domain Independent Lifted MAP Inference. - Happy Mittal; Prasoon Goyal; Vibhav Gogate; Parag Singla (NIPS, 2014)

An Integer Polynomial Programming Based Framework for Lifted MAP Inference. - Somdeb Sarkhel; Deepak Venugopal; Parag Singla; Vibhav Gogate (NIPS, 2014)

Scaling-up Importance Sampling for Markov Logic Networks. - Deepak Venugopal; Vibhav Gogate (NIPS, 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 (2), 2014)

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

Lifted Inference Rules With Constraints. - Happy Mittal; Anuj Mahajan; Vibhav Gogate; Parag Singla (NIPS, 2015)

Fast Lifted MAP Inference via Partitioning. - Somdeb Sarkhel; Parag Singla; Vibhav Gogate (NIPS, 2015)

Bounding the Cost of Search-Based Lifted Inference. - David B. Smith 002; Vibhav Gogate (NIPS, 2015)

On Parameter Tying by Quantization. - Li Chou; Somdeb Sarkhel; Nicholas Ruozzi; Vibhav Gogate (AAAI, 2016)

Learning Ensembles of Cutset Networks. - Tahrima Rahman; Vibhav Gogate (AAAI, 2016)

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

Joint Inference for Event Coreference Resolution. - Jing Lu; Deepak Venugopal; Vibhav Gogate; Vincent Ng 001 (COLING, 2016)

Probabilistic Inference Modulo Theories. - Rodrigo de Salvo Braz; Ciaran O'Reilly; Vibhav Gogate; Rina Dechter (IJCAI, 2016)

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

Probabilistic theorem proving. - Vibhav Gogate; Pedro M. Domingos (Commun. ACM, 2016)

Lifted Region-Based Belief Propagation. - David B. Smith 002; Parag Singla; Vibhav Gogate (CoRR, 2016)

Order Statistics for Probabilistic Graphical Models. - David B. Smith 002; Sara Rouhani; Vibhav Gogate (IJCAI, 2017)

Efficient Inference for Untied MLNs. - Somdeb Sarkhel; Deepak Venugopal; Nicholas Ruozzi; Vibhav Gogate (IJCAI, 2017)

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

Algorithms for the Nearest Assignment Problem. - Sara Rouhani; Tahrima Rahman; Vibhav Gogate (IJCAI, 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 (UAI, 2018)

Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization. - Yibo Yang; Nicholas Ruozzi; Vibhav Gogate (CoRR, 2018)

Domain Aware Markov Logic Networks. - Happy Mittal; Ayush Bhardwaj; Vibhav Gogate; Parag Singla (CoRR, 2018)

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

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

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

Explainable Activity Recognition in Videos. - Chiradeep Roy; Mahesh Shanbhag; Mahsan Nourani; Tahrima Rahman; Samia Kabir; Vibhav Gogate; Nicholas Ruozzi; Eric D. Ragan (IUI Workshops, 2019)

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 (CHI Extended Abstracts, 2020)

A Novel Approach for Constrained Optimization in Graphical Models. - Sara Rouhani; Tahrima Rahman; Vibhav Gogate (NeurIPS, 2020)

Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition. - Mahsan Nourani; Chiradeep Roy; Tahrima Rahman; Eric D. Ragan; Nicholas Ruozzi; Vibhav Gogate (CoRR, 2020)

Dynamic Cutset Networks. - Chiradeep Roy; Tahrima Rahman; Hailiang Dong; Nicholas Ruozzi; Vibhav Gogate (AISTATS, 2021)

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 (IUI, 2021)

Novel Upper Bounds for the Constrained Most Probable Explanation Task. - Tahrima Rahman; Sara Rouhani; Vibhav Gogate (NeurIPS, 2021)

Conditionally Tractable Density Estimation using Neural Networks. - Hailiang Dong; Chiradeep Roy; Tahrima Rahman; Vibhav Gogate; Nicholas Ruozzi (AISTATS, 2022)

Dissociation-Based Oblivious Bounds for Weighted Model Counting. - Vibhav Gogate (ISAIM, 2022)

Learning Tractable Probabilistic Models from Inconsistent Local Estimates. - Shasha Jin; Vasundhara Komaragiri; Tahrima Rahman; Vibhav Gogate (NeurIPS, 2022)

Robust learning of tractable probabilistic models. - Rohith Peddi; Tahrima Rahman; Vibhav Gogate (UAI, 2022)

DETOXER: A Visual Debugging Tool With Multiscope Explanations for Temporal Multilabel Classification. - Mahsan Nourani; Chiradeep Roy; Donald R. Honeycutt; Eric D. Ragan; Vibhav Gogate (IEEE Computer Graphics and Applications, 2022)

On the Importance of User Backgrounds and Impressions: Lessons Learned from Interactive AI Applications. - Mahsan Nourani; Chiradeep Roy; Jeremy E. Block; Donald R. Honeycutt; Tahrima Rahman; Eric D. Ragan; Vibhav Gogate (ACM Trans. Interact. Intell. Syst., 2022)

A New Modeling Framework for Continuous, Sequential Domains. - Hailiang Dong; James Amato; Vibhav Gogate; Nicholas Ruozzi (AISTATS, 2023)

Knowledge Intensive Learning of Cutset Networks. - Saurabh Mathur 002; Vibhav Gogate; Sriraam Natarajan (UAI, 2023)

Deep Dependency Networks for Multi-Label Classification. - Shivvrat Arya; Yu Xiang 001; Vibhav Gogate (CoRR, 2023)

CaptainCook4D: A dataset for understanding errors in procedural activities. - Rohith Peddi; Shivvrat Arya; Bharath Challa; Likhitha Pallapothula; Akshay Vyas; Jikai Wang; Qifan Zhang; Vasundhara Komaragiri; Eric D. Ragan; Nicholas Ruozzi; Yu Xiang 001; Vibhav Gogate (CoRR, 2023)

Explainable Activity Recognition in Videos using Deep Learning and Tractable Probabilistic Models. - Chiradeep Roy; Mahsan Nourani; Shivvrat Arya; Mahesh Shanbhag; Tahrima Rahman; Eric D. Ragan; Nicholas Ruozzi; Vibhav Gogate (ACM Trans. Interact. Intell. Syst., 2023)

Neural Network Approximators for Marginal MAP in Probabilistic Circuits. - Shivvrat Arya; Tahrima Rahman; Vibhav Gogate (AAAI, 2024)

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

Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification. - Shivvrat Arya; Yu Xiang 001; Vibhav Gogate (AISTATS, 2024)

Towards Scene Graph Anticipation. - Rohith Peddi; Saksham Singh; Saurabh; Parag Singla; Vibhav Gogate (ECCV (88), 2024)

Grasping Trajectory Optimization with Point Clouds. - Yu Xiang 001; Sai Haneesh Allu; Rohith Peddi; Tyler Summers; Vibhav Gogate (IROS, 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 D. Ragan; Nicholas Ruozzi; Yu Xiang 001; et al. (NeurIPS, 2024)

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

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 001; Eric D. Ragan (VR Workshops, 2024)

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

SINE: Scalable MPE Inference for Probabilistic Graphical Models using Advanced Neural Embeddings. - Shivvrat Arya; Tahrima Rahman; Vibhav Gogate (AISTATS, 2025)

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

Can Video Large Multimodal Models Think Like Doubters-or Double-Down: A Study on Defeasible Video Entailment. - Yue Zhang; Jilei Sun; Yunhui Guo; Vibhav Gogate (CoRR, 2025)