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Vibhav Gogate

35 papers · 2010–2025 · 10 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (16) 🧭 Keyword Pioneer 🌍 Conference Polyglot (10) πŸƒ Academic Marathon (15) 🐝 Cross-Pollinator (12) πŸ† Keyword Champion (4) 🀝 Dynamic Duo (12) 🧬 Topic Evolution πŸ”¬ Deep Specialist (17) πŸš€ Conference Pioneer πŸ’Ž Century Club (35) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (10) πŸ—ƒοΈ Keyword Collector (61) ⚑ Prolific Year (7)

Conferences

AISTATS (9) NIPS (8) IJCAI (6) UAI (4) AAAI (2) EMNLP (2) COLING (1) CVPR (1) ECCV (1) ICML (1)

Top co-authors

Tahrima Rahman (12) Nicholas Ruozzi (6) Deepak Venugopal (6) Shivvrat Arya (5) Parag Singla (4) Rohith Peddi (4) Sara Rouhani (4) Hailiang Dong (4) Vincent Ng (3) David Smith (3)

Keywords

probabilistic graphical model (8) graphical model (7) cutset network (5) probabilistic inference (5) probabilistic modeling (5) lifted inference (4) bayesian network (4) markov logic network (4) tractable inference (4) first-order logic (4) neural network (3) approximation algorithm (3) markov network (3) bayesian inference (3) map inference (3) most probable explanation (3) tractable probabilistic model (3) self-supervised learning (3) variational inference (2) probabilistic model (2)

Papers

Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven Optimization AAAI 2025
Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation CVPR 2025
Learning Distributionally Robust Tractable Probabilistic Models in Continuous Domains UAI 2024
A Neural Network Approach for Efficiently Answering Most Probable Explanation Queries in Probabilistic Models NIPS 2024
CaptainCook4D: A Dataset for Understanding Errors in Procedural Activities NIPS 2024
Neural Network Approximators for Marginal MAP in Probabilistic Circuits AAAI 2024
Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models AISTATS 2024
Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification AISTATS 2024
Towards Scene Graph Anticipation ECCV 2024
A New Modeling Framework for Continuous, Sequential Domains AISTATS 2023
Knowledge Intensive Learning of Cutset Networks UAI 2023
Learning Tractable Probabilistic Models from Inconsistent Local Estimates NIPS 2022
Conditionally Tractable Density Estimation using Neural Networks AISTATS 2022
Robust learning of tractable probabilistic models UAI 2022
Novel Upper Bounds for the Constrained Most Probable Explanation Task NIPS 2021
Dynamic Cutset Networks AISTATS 2021
A Novel Approach for Constrained Optimization in Graphical Models NIPS 2020
The 35th Uncertainty in Artificial Intelligence Conference: Preface UAI 2019
Domain-Size Aware Markov Logic Networks AISTATS 2019
Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models IJCAI 2019
Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation ICML 2019
Algorithms for the Nearest Assignment Problem IJCAI 2018
Efficient Inference for Untied MLNs IJCAI 2017
Order Statistics for Probabilistic Graphical Models IJCAI 2017
Advanced Markov Logic Techniques for Scalable Joint Inference in NLP EMNLP 2016
Joint Inference for Event Coreference Resolution COLING 2016
Probabilistic Inference Modulo Theories IJCAI 2016
Relieving the Computational Bottleneck: Joint Inference for Event Extraction with High-Dimensional Features EMNLP 2014
Lifted MAP Inference for Markov Logic Networks AISTATS 2014
Loopy Belief Propagation in the Presence of Determinism AISTATS 2014
The Inclusion-Exclusion Rule and Its Application to the Junction Tree Algorithm IJCAI 2013
On Lifting the Gibbs Sampling Algorithm NIPS 2012
Lifted Inference Seen from the Other Side : The Tractable Features NIPS 2010
Learning Efficient Markov Networks NIPS 2010
On Combining Graph-based Variance Reduction schemes AISTATS 2010