Vibhav Gogate
35 papers · 2010–2025 · 10 conferences · across top CS/AI conferences
Achievements
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πΊοΈ Taxonomy Completionist (16) π§ Keyword Pioneer π Renaissance Researcher (5) π Interdisciplinary Bridge π Conference Polyglot (10)
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Conference Polyglot
(10)
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Academic Marathon
(15)
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Cross-Pollinator
(12)
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Keyword Champion
(4)
π€
Dynamic Duo
(12)
π§¬
Topic Evolution
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Deep Specialist
(17)
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Conference Pioneer
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Century Club
(35)
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Trend Setter
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Unstoppable
(10)
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Keyword Collector
(61)
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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
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