Vibhav Gogate

发表

Order Statistics for Probabilistic Graphical Models

David B. Smith, Sara Rouhani, Vibhav Gogate, 2017, IJCAI.

Joint Inference for Event Coreference Resolution

Jing Lu, Vibhav Gogate, Vincent Ng, 2016, COLING.

A New Algorithm for Sampling CSP Solutions Uniformly at Random

Vibhav Gogate, Rina Dechter, 2006, CP.

Importance sampling-based estimation over AND/OR search spaces for graphical models

Vibhav Gogate, Rina Dechter, R. Dechter, 2012, Artif. Intell..

Lifted Inference Seen from the Other Side : The Tractable Features

Dan Suciu, Vibhav Gogate, Alexandra Meliou, 2010, NIPS.

Loopy Belief Propagation in the Presence of Determinism

David B. Smith, Vibhav Gogate, Vibhav Gogate, 2014, AISTATS.

Sampling-based lower bounds for counting queries

Vibhav Gogate, Rina Dechter, R. Dechter, 2011, Intelligenza Artificiale.

Studies in Solution Sampling

Vibhav Gogate, Rina Dechter, 2008, AAAI.

Approximate Inference in Probabilistic Graphical Models with Determinism

Vibhav Gogate, 2007, AAAI.

Probabilistic Inference Modulo Theories

Ciaran O'Reilly, Vibhav Gogate, Rina Dechter, 2016, IJCAI.

Structured Message Passing pdf

Pedro M. Domingos, Vibhav Gogate, 2013, UAI.

Lifted MAP Inference for Markov Logic Networks

Somdeb Sarkhel, Parag Singla, Vibhav Gogate, 2014, AISTATS.

Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models

Vibhav Gogate, Tahrima Rahman, Shasha Jin, 2019, IJCAI.

Approximation by Quantization pdf

Pedro M. Domingos, Vibhav Gogate, 2011, UAI.

Approximate Solution Sampling (and Counting) on AND/OR Spaces

Vibhav Gogate, Rina Dechter, 2008, CP.

Dissociation-Based Oblivious Bounds for Weighted Model Counting

Vibhav Gogate, Wolfgang Gatterbauer, Li Chou, 2018, UAI.

Relieving the Computational Bottleneck: Joint Inference for Event Extraction with High-Dimensional Features

Chen Chen, Vibhav Gogate, Vincent Ng, 2014, EMNLP.

On Parameter Tying by Quantization

Nicholas Ruozzi, Somdeb Sarkhel, Vibhav Gogate, 2016, AAAI.

Domain-Size Aware Markov Logic Networks

Parag Singla, Vibhav Gogate, Ayush Bhardwaj, 2019, AISTATS.

Dynamic Blocking and Collapsing for Gibbs Sampling pdf

Vibhav Gogate, Deepak Venugopal, D. Venugopal, 2013, UAI.

Probabilistic theorem proving

Pedro M. Domingos, Vibhav Gogate, Vibhav Gogate, 2011, UAI.

An Integer Polynomial Programming Based Framework for Lifted MAP Inference

Somdeb Sarkhel, Parag Singla, Vibhav Gogate, 2014, NIPS.

Advanced Markov Logic Techniques for Scalable Joint Inference in NLP

Vibhav Gogate, Vincent Ng, Deepak Venugopal, 2016, EMNLP 2016.

Automatic Parameter Tying: A New Approach for Regularized Parameter Learning in Markov Networks

Nicholas Ruozzi, Somdeb Sarkhel, Vibhav Gogate, 2018, AAAI.

GiSS: Combining Gibbs Sampling and SampleSearch for Inference in Mixed Probabilistic and Deterministic Graphical Models

Vibhav Gogate, Deepak Venugopal, D. Venugopal, 2013, AAAI.

Scalable Training of Markov Logic Networks Using Approximate Counting

Somdeb Sarkhel, Parag Singla, Vibhav Gogate, 2016, AAAI.

Exploiting Logical Structure in Lifted Probabilistic Inference

Pedro M. Domingos, Vibhav Gogate, Vibhav Gogate, 2010, StarAI@AAAI.

Domain Aware Markov Logic Networks pdf

Parag Singla, Vibhav Gogate, Ayush Bhardwaj, 2018, ArXiv.

AND/OR Importance Sampling pdf

Vibhav Gogate, Rina Dechter, 2008, UAI.

Lifted inference seen from the other side

Dan Suciu, Vibhav Gogate, Alexandra Meliou, 2010, NIPS 2010.

Efficient Inference for Untied MLNs

Nicholas Ruozzi, Somdeb Sarkhel, Vibhav Gogate, 2017, IJCAI.

A Unified Approach to Abductive Inference

Henry Kautz, Thomas G. Dietterich, Joshua B. Tenenbaum, 2014 .

Automatic Parameter Tying in Neural Networks

Nicholas Ruozzi, Yibo Yang, Vibhav Gogate, 2018 .

Join-Graph Propagation Algorithms

Vibhav Gogate, Rina Dechter, Robert Mateescu, 2010, J. Artif. Intell. Res..

Counting-Based Look-Ahead Schemes for Constraint Satisfaction

Vibhav Gogate, Rina Dechter, Kalev Kask, 2004, CP.

Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation

Vibhav Gogate, Tahrima Rahman, Shasha Jin, 2019, ICML.

Bounding the Cost of Search-Based Lifted Inference

David B. Smith, Vibhav Gogate, Vibhav Gogate, 2015, NIPS.

Lifted Region-Based Belief Propagation pdf

Parag Singla, David B. Smith, Vibhav Gogate, 2016, ArXiv.

Scaling-up Importance Sampling for Markov Logic Networks

Vibhav Gogate, Deepak Venugopal, D. Venugopal, 2014, NIPS.

Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints pdf

Vibhav Gogate, Rina Dechter, 2005, UAI.

Cutset Networks: A Simple, Tractable, and Scalable Approach for Improving the Accuracy of Chow-Liu Trees

Vibhav Gogate, Prasanna V. Kothalkar, Tahrima Rahman, 2014, ECML/PKDD.

On Lifting the Gibbs Sampling Algorithm

Vibhav Gogate, Deepak Venugopal, D. Venugopal, 2012, StarAI@UAI.

Lifting WALKSAT-Based Local Search Algorithms for MAP Inference

Somdeb Sarkhel, Vibhav Gogate, Vibhav Gogate, 2013, StarAI@AAAI.

Lifted Marginal MAP Inference pdf

Parag Singla, Vishal Sharma, Vibhav Gogate, 2018, UAI.

Modeling Transportation Routines using Hybrid Dynamic Mixed Networks pdf

Vibhav Gogate, Rina Dechter, Bozhena Bidyuk, 2005, UAI.

Formula-Based Probabilistic Inference pdf

Pedro M. Domingos, Vibhav Gogate, Vibhav Gogate, 2010, UAI.

The AAAI-13 Conference Workshops

Diane J. Cook, Christopher W. Geib, Gita Reese Sukthankar, 2013, AI Mag..

Explainable Activity Recognition in Videos

Nicholas Ruozzi, Eric D. Ragan, Vibhav Gogate, 2019, IUI Workshops.

SampleSearch: A Scheme that Searches for Consistent Samples

Vibhav Gogate, Rina Dechter, 2007, AISTATS.

Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization pdf

Nicholas Ruozzi, Vibhav Gogate, Yibo Yang, 2018, ArXiv.

Learning Efficient Markov Networks

Pedro M. Domingos, Vibhav Gogate, William Austin Webb, 2010, NIPS.

Evidence-Based Clustering for Scalable Inference in Markov Logic

Vibhav Gogate, Deepak Venugopal, 2014, ECML/PKDD.

Learning Ensembles of Cutset Networks

Vibhav Gogate, Tahrima Rahman, 2016, AAAI.

Investigating the Importance of First Impressions and Explainable AI with Interactive Video Analysis

Eric D. Ragan, Vibhav Gogate, Tahrima Rahman, 2020, CHI Extended Abstracts.

A Novel Approach for Constrained Optimization in Graphical Models

Vibhav Gogate, Tahrima Rahman, Sara Rouhani, 2020, NeurIPS.

Merging Strategies for Sum-Product Networks: From Trees to Graphs

Vibhav Gogate, Tahrima Rahman, Vibhav Gogate, 2016, UAI.

Studies in Lower Bounding Probabilities of Evidence using the Markov Inequality pdf

Vibhav Gogate, Rina Dechter, Bozhena Bidyuk, 2007, UAI.

Just Count the Satisfied Groundings: Scalable Local-Search and Sampling Based Inference in MLNs

Somdeb Sarkhel, Vibhav Gogate, Deepak Venugopal, 2015, AAAI.

Advances in Lifted Importance Sampling

Vibhav Gogate, Deepak Venugopal, Abhay Jha, 2012, AAAI.

Fast Lifted MAP Inference via Partitioning

Somdeb Sarkhel, Parag Singla, Vibhav Gogate, 2015, NIPS.

Algorithms for the Nearest Assignment Problem

Sara Rouhani, Vibhav Gogate, Tahrima Rahman, 2018, IJCAI.

Approximate Counting by Sampling the Backtrack-free Search Space

Vibhav Gogate, Rina Dechter, 2007, AAAI.

The Inclusion-Exclusion Rule and its Application to the Junction Tree Algorithm

David B. Smith, Vibhav Gogate, Vibhav Gogate, 2013, IJCAI.

New Look-Ahead Schemes for Constraint Satisfaction

Vibhav Gogate, Rina Dechter, Kalev Kask, 2004, ISAIM.

New Rules for Domain Independent Lifted MAP Inference

Prasoon Goyal, Parag Singla, Vibhav Gogate, 2014, NIPS.

A Complete Anytime Algorithm for Treewidth

Vibhav Gogate, Rina Dechter, R. Dechter, 2004, UAI.

Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition pdf

Nicholas Ruozzi, Eric D. Ragan, Vibhav Gogate, 2020, ArXiv.

On Combining Graph-based Variance Reduction schemes

Vibhav Gogate, Rina Dechter, 2010, AISTATS.

Lifted Inference Rules With Constraints

Parag Singla, Vibhav Gogate, Anuj Mahajan, 2015, NIPS.

SampleSearch: Importance sampling in presence of determinism

Vibhav Gogate, Rina Dechter, R. Dechter, 2011, Artif. Intell..

Dynamic Cutset Networks

Nicholas Ruozzi, Vibhav Gogate, Tahrima Rahman, 2021, AISTATS.

Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems

Vibhav Gogate, Tahrima Rahman, Donald R. Honeycutt, 2021, IUI.

Sampling algorithms for probabilistic graphical models with determinism

Vibhav Gogate, Rina Dechter, R. Dechter, 2009 .

A Simple Application of Sampling Importance Resampling ( SIR ) for Solution Sampling

Vibhav Gogate, Rina Dechter, R. Dechter, 2007 .

Approximate Solution Sampling ( and Counting ) on AND / OR search space

Vibhav Gogate, Rina Dechter, R. Dechter, 2008 .