Somdeb Sarkhel

发表

Inferring Individual Level Causal Models from Graph-based Relational Time Series pdf

Ryan A. Rossi, Somdeb Sarkhel, David Arbour, 2020, ArXiv.

Scalable Bid Landscape Forecasting in Real-time Bidding

Aritra Ghosh, Somdeb Sarkhel, Viswanathan Swaminathan, 2019, ECML/PKDD.

Lifted MAP Inference for Markov Logic Networks

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

Optimal Bidding Strategy without Exploration in Real-time Bidding pdf

Aritra Ghosh, Somdeb Sarkhel, Viswanathan Swaminathan, 2020, SDM.

On Parameter Tying by Quantization

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

An Integer Polynomial Programming Based Framework for Lifted MAP Inference

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

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

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

Scalable Training of Markov Logic Networks Using Approximate Counting

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

Content-Based Effectiveness Prediction of Video Advertisements

Somdeb Sarkhel, Viswanathan Swaminathan, Saayan Mitra, 2018, 2018 IEEE International Symposium on Multimedia (ISM).

Adaptive Rao-Blackwellisation in Gibbs Sampling for Probabilistic Graphical Models

Somdeb Sarkhel, Deepak Venugopal, Craig Kelly, 2019, AISTATS.

Efficient Inference for Untied MLNs

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

Scaling-Up MAP and Marginal MAP Inference in Markov Logic

Somdeb Sarkhel, 2016, AAAI.

Lifting WALKSAT-Based Local Search Algorithms for MAP Inference

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

User Segment Identification Based on Similarity in Content Consumption

Somdeb Sarkhel, Viswanathan Swaminathan, Wreetabrata Kar, 2017, 2017 IEEE International Symposium on Multimedia (ISM).

Efficient Weight Learning in High-Dimensional Untied MLNs

Somdeb Sarkhel, Deepak Venugopal, Khan Mohammad Al Farabi, 2018, AISTATS.

Fine-Grained Explanations Using Markov Logic

Somdeb Sarkhel, Deepak Venugopal, Sanorita Dey, 2019, ECML/PKDD.

Scaling Dnn-Based Video Analysis By Coarse-Grained And Fine-Grained Parallelism

Somdeb Sarkhel, Saayan Mitra, Phanwadee Sinthong, 2020, 2020 IEEE International Conference on Multimedia and Expo (ICME).

Personalized Video Recommendations for Shared Accounts

Shuo Yang, Somdeb Sarkhel, Viswanathan Swaminathan, 2017, 2017 IEEE International Symposium on Multimedia (ISM).

Scalable, Lifted Maximum a Posteriori Inference

Somdeb Sarkhel, 2016 .

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

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

Scaling up Inference in MLNs with Spark

Somdeb Sarkhel, Deepak Venugopal, Mohammad Maminur Islam, 2018, 2018 IEEE International Conference on Big Data (Big Data).

Non-parametric Domain Approximation for Scalable Gibbs Sampling in MLNs

Somdeb Sarkhel, Deepak Venugopal, Kyle Cherry, 2016, UAI.

Fast Lifted MAP Inference via Partitioning

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

Learning Mixtures of MLNs

Somdeb Sarkhel, Deepak Venugopal, Mohammad Maminur Islam, 2018, AAAI.

On Lifted Inference Using Neural Embeddings

Somdeb Sarkhel, Deepak Venugopal, Maminur Islam, 2019, AAAI.

Contrastive Learning in Neural Tensor Networks using Asymmetric Examples

Somdeb Sarkhel, Deepak Venugopal, Mohammad Maminur Islam, 2021, 2021 IEEE International Conference on Big Data (Big Data).