Vibhav Gogate

Orcid: 0000-0002-6459-7358

Affiliations:
  • University of Texas at Dallas, Department of Computer Science


According to our database1, Vibhav Gogate authored at least 88 papers between 2004 and 2024.

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Bibliography

2024
Grasping Trajectory Optimization with Point Clouds.
CoRR, 2024

Predictive Task Guidance with Artificial Intelligence in Augmented Reality.
Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, 2024

Towards Scene Graph Anticipation.
Proceedings of the Computer Vision - ECCV 2024, 2024

Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Neural Network Approximators for Marginal MAP in Probabilistic Circuits.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Explainable Activity Recognition in Videos using Deep Learning and Tractable Probabilistic Models.
ACM Trans. Interact. Intell. Syst., December, 2023

CaptainCook4D: A dataset for understanding errors in procedural activities.
CoRR, 2023

Deep Dependency Networks for Multi-Label Classification.
CoRR, 2023

Knowledge Intensive Learning of Cutset Networks.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

A New Modeling Framework for Continuous, Sequential Domains.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
On the Importance of User Backgrounds and Impressions: Lessons Learned from Interactive AI Applications.
ACM Trans. Interact. Intell. Syst., December, 2022

DETOXER: A Visual Debugging Tool With Multiscope Explanations for Temporal Multilabel Classification.
IEEE Computer Graphics and Applications, 2022

Robust learning of tractable probabilistic models.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Learning Tractable Probabilistic Models from Inconsistent Local Estimates.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Dissociation-Based Oblivious Bounds for Weighted Model Counting.
Proceedings of the International Symposium on Artificial Intelligence and Mathematics 2022 (ISAIM 2022), 2022

Conditionally Tractable Density Estimation using Neural Networks.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Novel Upper Bounds for the Constrained Most Probable Explanation Task.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems.
Proceedings of the IUI '21: 26th International Conference on Intelligent User Interfaces, 2021

Dynamic Cutset Networks.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition.
CoRR, 2020

A Novel Approach for Constrained Optimization in Graphical Models.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Investigating the Importance of First Impressions and Explainable AI with Interactive Video Analysis.
Proceedings of the Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 2020

2019
Explainable Activity Recognition in Videos.
Proceedings of the Joint Proceedings of the ACM IUI 2019 Workshops co-located with the 24th ACM Conference on Intelligent User Interfaces (ACM IUI 2019), 2019

Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation.
Proceedings of the 36th International Conference on Machine Learning, 2019

Domain-Size Aware Markov Logic Networks.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Domain Aware Markov Logic Networks.
CoRR, 2018

Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization.
CoRR, 2018

Lifted Marginal MAP Inference.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Dissociation-Based Oblivious Bounds for Weighted Model Counting.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Algorithms for the Nearest Assignment Problem.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Automatic Parameter Tying: A New Approach for Regularized Parameter Learning in Markov Networks.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Efficient Inference for Untied MLNs.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Order Statistics for Probabilistic Graphical Models.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

2016
Lifted Region-Based Belief Propagation.
CoRR, 2016

Probabilistic theorem proving.
Commun. ACM, 2016

Merging Strategies for Sum-Product Networks: From Trees to Graphs.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Probabilistic Inference Modulo Theories.
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

Joint Inference for Event Coreference Resolution.
Proceedings of the COLING 2016, 2016

Scalable Training of Markov Logic Networks Using Approximate Counting.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

Learning Ensembles of Cutset Networks.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

On Parameter Tying by Quantization.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
Bounding the Cost of Search-Based Lifted Inference.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Fast Lifted MAP Inference via Partitioning.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Lifted Inference Rules With Constraints.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Just Count the Satisfied Groundings: Scalable Local-Search and Sampling Based Inference in MLNs.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014
Cutset Networks: A Simple, Tractable, and Scalable Approach for Improving the Accuracy of Chow-Liu Trees.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

Scaling-up Importance Sampling for Markov Logic Networks.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

An Integer Polynomial Programming Based Framework for Lifted MAP Inference.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

New Rules for Domain Independent Lifted MAP Inference.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Relieving the Computational Bottleneck: Joint Inference for Event Extraction with High-Dimensional Features.
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, 2014

Loopy Belief Propagation in the Presence of Determinism.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

Lifted MAP Inference for Markov Logic Networks.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

Evidence-Based Clustering for Scalable Inference in Markov Logic.
Proceedings of the Statistical Relational Artificial Intelligence, 2014

2013
The AAAI-13 Conference Workshops.
AI Mag., 2013

Dynamic Blocking and Collapsing for Gibbs Sampling.
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Structured Message Passing.
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

The Inclusion-Exclusion Rule and its Application to the Junction Tree Algorithm.
Proceedings of the IJCAI 2013, 2013

GiSS: Combining Gibbs Sampling and SampleSearch for Inference in Mixed Probabilistic and Deterministic Graphical Models.
Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013

Lifting WALKSAT-Based Local Search Algorithms for MAP Inference.
Proceedings of the Statistical Relational Artificial Intelligence, 2013

Preface.
Proceedings of the Statistical Relational Artificial Intelligence, 2013

Organizers.
Proceedings of the Statistical Relational Artificial Intelligence, 2013

2012
Importance sampling-based estimation over AND/OR search spaces for graphical models.
Artif. Intell., 2012

On Lifting the Gibbs Sampling Algorithm.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Advances in Lifted Importance Sampling.
Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012

2011
Sampling-based lower bounds for counting queries.
Intelligenza Artificiale, 2011

SampleSearch: Importance sampling in presence of determinism.
Artif. Intell., 2011

Approximation by Quantization.
Proceedings of the UAI 2011, 2011

2010
On Combining Graph-based Variance Reduction schemes.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Join-Graph Propagation Algorithms.
J. Artif. Intell. Res., 2010

Formula-Based Probabilistic Inference.
Proceedings of the UAI 2010, 2010

Lifted Inference Seen from the Other Side : The Tractable Features.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Learning Efficient Markov Networks.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Exploiting Logical Structure in Lifted Probabilistic Inference.
Proceedings of the Statistical Relational Artificial Intelligence, 2010

2008
AND/OR Importance Sampling.
Proceedings of the UAI 2008, 2008

Approximate Solution Sampling (and Counting) on AND/OR Spaces.
Proceedings of the Principles and Practice of Constraint Programming, 2008

Studies in Solution Sampling.
Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, 2008

2007
SampleSearch: A Scheme that Searches for Consistent Samples.
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Studies in Lower Bounding Probabilities of Evidence using the Markov Inequality.
Proceedings of the UAI 2007, 2007

Approximate Counting by Sampling the Backtrack-free Search Space.
Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 2007

Approximate Inference in Probabilistic Graphical Models with Determinism.
Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 2007

2006
A New Algorithm for Sampling CSP Solutions Uniformly at Random.
Proceedings of the Principles and Practice of Constraint Programming, 2006

2005
Modeling Transportation Routines using Hybrid Dynamic Mixed Networks.
Proceedings of the UAI '05, 2005

Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints.
Proceedings of the UAI '05, 2005

2004
A Complete Anytime Algorithm for Treewidth.
Proceedings of the UAI '04, 2004

New Look-Ahead Schemes for Constraint Satisfaction.
Proceedings of the International Symposium on Artificial Intelligence and Mathematics, 2004

Counting-Based Look-Ahead Schemes for Constraint Satisfaction.
Proceedings of the Principles and Practice of Constraint Programming, 2004


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