Algorithms for k-median Clustering over Distributed Streams. - Sutanu Gayen; N. V. Vinodchandran (COCOON, 2016)

KmerEstimate: A Streaming Algorithm for Estimating k-mer Counts with Optimal Space Usage. - Sairam Behera; Sutanu Gayen; Jitender S. Deogun; N. V. Vinodchandran (BCB, 2018)

New Algorithms for Distributed Sliding Windows. - Sutanu Gayen; N. V. Vinodchandran (SWAT, 2018)

Learning and Sampling of Atomic Interventions from Observations. - Arnab Bhattacharyya 001; Sutanu Gayen; Saravanan Kandasamy 002; Ashwin Maran; N. Variyam Vinodchandran (ICML, 2020)

Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning. - Arnab Bhattacharyya 001; Sutanu Gayen; Kuldeep S. Meel; N. V. Vinodchandran (NeurIPS, 2020)

Perfect Zero Knowledge: New Upperbounds and Relativized Separations. - Peter Dixon 002; Sutanu Gayen; Aduri Pavan; N. V. Vinodchandran (TCC (1), 2020)

Efficiently Learning and Sampling Interventional Distributions from Observations. - Arnab Bhattacharyya 001; Sutanu Gayen; Saravanan Kandasamy 002; Ashwin Maran; N. V. Vinodchandran (CoRR, 2020)

Testing Product Distributions: A Closer Look. - Arnab Bhattacharyya 001; Sutanu Gayen; Saravanan Kandasamy 002; N. V. Vinodchandran (ALT, 2021)

Near-optimal learning of tree-structured distributions by Chow-Liu. - Arnab Bhattacharyya 001; Sutanu Gayen; Eric Price 001; N. V. Vinodchandran (STOC, 2021)

Efficient inference of interventional distributions. - Arnab Bhattacharyya 001; Sutanu Gayen; Saravanan Kandasamy 002; Vedant Raval; N. V. Vinodchandran (CoRR, 2021)

Learning Sparse Fixed-Structure Gaussian Bayesian Networks. - Arnab Bhattacharyya 001; Davin Choo; Rishikesh Gajjala; Sutanu Gayen; Yuhao Wang (AISTATS, 2022)

Efficient interventional distribution learning in the PAC framework. - Arnab Bhattacharyya 001; Sutanu Gayen; Saravanan Kandasamy 002; Vedant Raval; N. Variyam Vinodchandran (AISTATS, 2022)

On Approximating Total Variation Distance. - Arnab Bhattacharyya 001; Sutanu Gayen; Kuldeep S. Meel; Dimitrios Myrisiotis; A. Pavan 001; N. V. Vinodchandran (IJCAI, 2023)

Total Variation Distance Estimation Is as Easy as Probabilistic Inference. - Arnab Bhattacharyya 001; Sutanu Gayen; Kuldeep S. Meel; Dimitrios Myrisiotis; A. Pavan 001; N. V. Vinodchandran (Electron. Colloquium Comput. Complex., 2023)

Near-Optimal Learning of Tree-Structured Distributions by Chow and Liu. - Arnab Bhattacharyya 001; Sutanu Gayen; Eric Price 001; Vincent Y. F. Tan; N. V. Vinodchandran (SIAM J. Comput., 2023)

Total Variation Distance Meets Probabilistic Inference. - Arnab Bhattacharyya 001; Sutanu Gayen; Kuldeep S. Meel; Dimitrios Myrisiotis; A. Pavan 001; N. V. Vinodchandran (ICML, 2024)

Distribution Learning Meets Graph Structure Sampling. - Arnab Bhattacharyya 001; Sutanu Gayen; Philips George John; Sayantan Sen; N. V. Vinodchandran (CoRR, 2024)

Efficient Sample-optimal Learning of Gaussian Tree Models via Sample-optimal Testing of Gaussian Mutual Information. - Sutanu Gayen; Sanket Kale; Sayantan Sen (CoRR, 2024)

Learnability of Parameter-Bounded Bayes Nets. - Arnab Bhattacharyya 001; Davin Choo; Sutanu Gayen; Dimitrios Myrisiotis (AAAI, 2025)

Computational Explorations of Total Variation Distance. - Arnab Bhattacharyya 001; Sutanu Gayen; Kuldeep S. Meel; Dimitrios Myrisiotis; Aduri Pavan; N. V. Vinodchandran (ICLR, 2025)

Total variation distance for product distributions is #P-complete. - Arnab Bhattacharyya 001; Sutanu Gayen; Kuldeep S. Meel; Dimitrios Myrisiotis; A. Pavan 001; N. V. Vinodchandran (Inf. Process. Lett., 2025)