Vibhav Gogate

ORCID

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 48

Top Venues

Publications

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)
Shivvrat Arya, Yu Xiang, Vibhav Gogate
Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification. AISTATS (2024)
Shivvrat Arya, Tahrima Rahman, Vibhav Gogate
Neural Network Approximators for Marginal MAP in Probabilistic Circuits. AAAI (2024)
Rohith Peddi, Saksham Singh, Saurabh, Parag Singla, Vibhav Gogate
Towards Scene Graph Anticipation. CoRR (2024)
Shivvrat Arya, Tahrima Rahman, Vibhav Gogate
Neural Network Approximators for Marginal MAP in Probabilistic Circuits. CoRR (2024)
Yu Xiang, Sai Haneesh Allu, Rohith Peddi, Tyler Summers, Vibhav Gogate
Grasping Trajectory Optimization with Point Clouds. CoRR (2024)
Shivvrat Arya, Tahrima Rahman, Vibhav Gogate
Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models. AISTATS (2024)
Shivvrat Arya, Yu Xiang, Vibhav Gogate
Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification. CoRR (2024)
Shivvrat Arya, Tahrima Rahman, Vibhav Gogate
Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models. CoRR (2024)
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) (2023)
Saurabh Mathur, Vibhav Gogate, Sriraam Natarajan
Knowledge Intensive Learning of Cutset Networks. UAI (2023)
Shivvrat Arya, Yu Xiang, Vibhav Gogate
Deep Dependency Networks for Multi-Label Classification. CoRR (2023)
Hailiang Dong, James Amato, Vibhav Gogate, Nicholas Ruozzi
A New Modeling Framework for Continuous, Sequential Domains. AISTATS (2023)
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 (2023)
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) (2022)
Hailiang Dong, Chiradeep Roy, Tahrima Rahman, Vibhav Gogate, Nicholas Ruozzi
Conditionally Tractable Density Estimation using Neural Networks. AISTATS (2022)
Vibhav Gogate
Dissociation-Based Oblivious Bounds for Weighted Model Counting. ISAIM (2022)
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) (2022)
Shasha Jin, Vasundhara Komaragiri, Tahrima Rahman, Vibhav Gogate
Learning Tractable Probabilistic Models from Inconsistent Local Estimates. NeurIPS (2022)
Rohith Peddi, Tahrima Rahman, Vibhav Gogate
Robust learning of tractable probabilistic models. UAI (2022)
Chiradeep Roy, Tahrima Rahman, Hailiang Dong, Nicholas Ruozzi, Vibhav Gogate
Dynamic Cutset Networks. AISTATS (2021)
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)
Tahrima Rahman, Sara Rouhani, Vibhav Gogate
Novel Upper Bounds for the Constrained Most Probable Explanation Task. NeurIPS (2021)
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)
Sara Rouhani, Tahrima Rahman, Vibhav Gogate
A Novel Approach for Constrained Optimization in Graphical Models. NeurIPS (2020)
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 (2020)
Happy Mittal, Ayush Bhardwaj, Vibhav Gogate, Parag Singla
Domain-Size Aware Markov Logic Networks. AISTATS (2019)
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)
Tahrima Rahman, Shasha Jin, Vibhav Gogate
Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation. ICML (2019)
Tahrima Rahman, Shasha Jin, Vibhav Gogate
Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models. IJCAI (2019)
Yibo Yang, Nicholas Ruozzi, Vibhav Gogate
Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization. CoRR (2018)
Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate, Parag Singla
Lifted Marginal MAP Inference. UAI (2018)
Li Chou, Wolfgang Gatterbauer, Vibhav Gogate
Dissociation-Based Oblivious Bounds for Weighted Model Counting. UAI (2018)
Happy Mittal, Ayush Bhardwaj, Vibhav Gogate, Parag Singla
Domain Aware Markov Logic Networks. CoRR (2018)
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)
Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate, Parag Singla
Lifted Marginal MAP Inference. CoRR (2018)
Sara Rouhani, Tahrima Rahman, Vibhav Gogate
Algorithms for the Nearest Assignment Problem. IJCAI (2018)
David B. Smith, Sara Rouhani, Vibhav Gogate
Order Statistics for Probabilistic Graphical Models. IJCAI (2017)
Somdeb Sarkhel, Deepak Venugopal, Nicholas Ruozzi, Vibhav Gogate
Efficient Inference for Untied MLNs. IJCAI (2017)
David B. Smith, Parag Singla, Vibhav Gogate
Lifted Region-Based Belief Propagation. CoRR (2016)
Li Chou, Somdeb Sarkhel, Nicholas Ruozzi, Vibhav Gogate
On Parameter Tying by Quantization. AAAI (2016)
Tahrima Rahman, Vibhav Gogate
Merging Strategies for Sum-Product Networks: From Trees to Graphs. UAI (2016)
Tahrima Rahman, Vibhav Gogate
Learning Ensembles of Cutset Networks. AAAI (2016)
Rodrigo de Salvo Braz, Ciaran O'Reilly, Vibhav Gogate, Rina Dechter
Probabilistic Inference Modulo Theories. CoRR (2016)
Rodrigo de Salvo Braz, Ciaran O'Reilly, Vibhav Gogate, Rina Dechter
Probabilistic Inference Modulo Theories. IJCAI (2016)
Vibhav Gogate, Pedro M. Domingos
Probabilistic theorem proving. Commun. ACM 59 (7) (2016)
Somdeb Sarkhel, Deepak Venugopal, Tuan Anh Pham, Parag Singla, Vibhav Gogate
Scalable Training of Markov Logic Networks Using Approximate Counting. AAAI (2016)
Jing Lu, Deepak Venugopal, Vibhav Gogate, Vincent Ng
Joint Inference for Event Coreference Resolution. COLING (2016)
David B. Smith, Vibhav Gogate
Bounding the Cost of Search-Based Lifted Inference. NIPS (2015)
Deepak Venugopal, Somdeb Sarkhel, Vibhav Gogate
Just Count the Satisfied Groundings: Scalable Local-Search and Sampling Based Inference in MLNs. AAAI (2015)
Somdeb Sarkhel, Parag Singla, Vibhav Gogate
Fast Lifted MAP Inference via Partitioning. NIPS (2015)
Happy Mittal, Anuj Mahajan, Vibhav Gogate, Parag Singla
Lifted Inference Rules With Constraints. NIPS (2015)
Tahrima Rahman, Prasanna Kothalkar, Vibhav Gogate
Cutset Networks: A Simple, Tractable, and Scalable Approach for Improving the Accuracy of Chow-Liu Trees. ECML/PKDD (2) (2014)
Deepak Venugopal, Chen Chen, Vibhav Gogate, Vincent Ng
Relieving the Computational Bottleneck: Joint Inference for Event Extraction with High-Dimensional Features. EMNLP (2014)
Deepak Venugopal, Vibhav Gogate
Evidence-Based Clustering for Scalable Inference in Markov Logic. ECML/PKDD (3) (2014)
Deepak Venugopal, Vibhav Gogate
Evidence-Based Clustering for Scalable Inference in Markov Logic. AAAI Workshop: Statistical Relational Artificial Intelligence (2014)
Robert Mateescu, Kalev Kask, Vibhav Gogate, Rina Dechter
Join-Graph Propagation Algorithms. CoRR (2014)
Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav Gogate
An Integer Polynomial Programming Based Framework for Lifted MAP Inference. NIPS (2014)
Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav Gogate
Lifted MAP Inference for Markov Logic Networks. AISTATS (2014)
Happy Mittal, Prasoon Goyal, Vibhav Gogate, Parag Singla
New Rules for Domain Independent Lifted MAP Inference. NIPS (2014)
David B. Smith, Vibhav Gogate
Loopy Belief Propagation in the Presence of Determinism. AISTATS (2014)
Deepak Venugopal, Vibhav Gogate
Scaling-up Importance Sampling for Markov Logic Networks. NIPS (2014)
Vibhav Gogate, Pedro M. Domingos
Structured Message Passing. CoRR (2013)
Somdeb Sarkhel, Vibhav Gogate
Lifting WALKSAT-Based Local Search Algorithms for MAP Inference. AAAI Workshop: Statistical Relational Artificial Intelligence (2013)
Vikas Agrawal, Christopher Archibald, Mehul Bhatt, Hung Hai 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) (2013)
Vibhav Gogate
Organizers. AAAI Workshop: Statistical Relational Artificial Intelligence (2013)
Deepak Venugopal, Vibhav Gogate
GiSS: Combining Gibbs Sampling and SampleSearch for Inference in Mixed Probabilistic and Deterministic Graphical Models. AAAI (2013)
David B. Smith, Vibhav Gogate
The Inclusion-Exclusion Rule and its Application to the Junction Tree Algorithm. IJCAI (2013)
Vibhav Gogate, Pedro M. Domingos
Structured Message Passing. UAI (2013)
Deepak Venugopal, Vibhav Gogate
Dynamic Blocking and Collapsing for Gibbs Sampling. UAI (2013)
Vibhav Gogate, Kristian Kersting, Sriraam Natarajan, David Poole
Preface. AAAI Workshop: Statistical Relational Artificial Intelligence (2013)
Deepak Venugopal, Vibhav Gogate
Dynamic Blocking and Collapsing for Gibbs Sampling. CoRR (2013)
Vibhav Gogate, Rina Dechter
AND/OR Importance Sampling CoRR (2012)
Vibhav Gogate, Rina Dechter
Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints CoRR (2012)
Deepak Venugopal, Vibhav Gogate
On Lifting the Gibbs Sampling Algorithm. NIPS (2012)
Vibhav Gogate, Abhay Kumar Jha, Deepak Venugopal
Advances in Lifted Importance Sampling. AAAI (2012)
Vibhav Gogate, Rina Dechter, Bozhena Bidyuk, Craig Rindt, James Marca
Modeling Transportation Routines using Hybrid Dynamic Mixed Networks CoRR (2012)
Vibhav Gogate, Pedro M. Domingos
Formula-Based Probabilistic Inference CoRR (2012)
Vibhav Gogate, Pedro M. Domingos
Approximation by Quantization CoRR (2012)
Vibhav Gogate, Rina Dechter
Importance sampling-based estimation over AND/OR search spaces for graphical models. Artif. Intell. (2012)
Vibhav Gogate, Pedro M. Domingos
Probabilistic Theorem Proving CoRR (2012)
Vibhav Gogate, Bozhena Bidyuk, Rina Dechter
Studies in Lower Bounding Probabilities of Evidence using the Markov Inequality CoRR (2012)
Deepak Venugopal, Vibhav Gogate
On Lifting the Gibbs Sampling Algorithm. StarAI@UAI (2012)
Vibhav Gogate, Rina Dechter
A Complete Anytime Algorithm for Treewidth CoRR (2012)
Vibhav Gogate, Rina Dechter
Sampling-based lower bounds for counting queries. Intelligenza Artificiale 5 (2) (2011)
Vibhav Gogate, Pedro M. Domingos
Approximation by Quantization. UAI (2011)
Vibhav Gogate, Pedro M. Domingos
Probabilistic Theorem Proving. UAI (2011)
Vibhav Gogate, Rina Dechter
SampleSearch: Importance sampling in presence of determinism. Artif. Intell. 175 (2) (2011)
Robert Mateescu, Kalev Kask, Vibhav Gogate, Rina Dechter
Join-Graph Propagation Algorithms. J. Artif. Intell. Res. 37 (2010)
Vibhav Gogate, Rina Dechter
On Combining Graph-based Variance Reduction schemes. AISTATS (2010)
Abhay Kumar Jha, Vibhav Gogate, Alexandra Meliou, Dan Suciu
Lifted Inference Seen from the Other Side : The Tractable Features. NIPS (2010)
Vibhav Gogate, Pedro M. Domingos
Exploiting Logical Structure in Lifted Probabilistic Inference. Statistical Relational Artificial Intelligence (2010)
Vibhav Gogate, Pedro M. Domingos
Formula-Based Probabilistic Inference. UAI (2010)
Vibhav Gogate, William Austin Webb, Pedro M. Domingos
Learning Efficient Markov Networks. NIPS (2010)
Vibhav Gogate, Rina Dechter
Studies in Solution Sampling. AAAI (2008)
Vibhav Gogate, Rina Dechter
AND/OR Importance Sampling. UAI (2008)
Vibhav Gogate, Rina Dechter
Approximate Solution Sampling (and Counting) on AND/OR Spaces. CP (2008)
Vibhav Gogate, Bozhena Bidyuk, Rina Dechter
Studies in Lower Bounding Probabilities of Evidence using the Markov Inequality. UAI (2007)
Vibhav Gogate, Rina Dechter
Approximate Counting by Sampling the Backtrack-free Search Space. AAAI (2007)
Vibhav Gogate
Approximate Inference in Probabilistic Graphical Models with Determinism. AAAI (2007)

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Publication Activity (10 Years)

Top Topics

Top Venues

Vibhav Gogate