Bhattacharyya, Arnab

32 publications

AISTATS 2025 Approximating the Total Variation Distance Between Gaussians Arnab Bhattacharyya, Weiming Feng, Piyush Srivastava

ICLR 2025 Computational Explorations of Total Variation Distance Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, A. Pavan, N. V. Vinodchandran

NeurIPS 2025 Distribution Learning Meets Graph Structure Sampling Arnab Bhattacharyya, Sutanu Gayen, Philips George John, Sayantan Sen, N. V. Vinodchandran

AAAI 2025 Learnability of Parameter-Bounded Bayes Nets Arnab Bhattacharyya, Davin Choo, Sutanu Gayen, Dimitrios Myrisiotis

AISTATS 2025 Learning High-Dimensional Gaussians from Censored Data Arnab Bhattacharyya, Constantinos Costis Daskalakis, Themis Gouleakis, Yuhao Wang

ICML 2025 Learning Multivariate Gaussians with Imperfect Advice Arnab Bhattacharyya, Davin Choo, Philips George John, Themis Gouleakis

CLeaR 2025 Probably Approximately Correct High-Dimensional Causal Effect Estimation Given a Valid Adjustment Set Davin Choo, Chandler Squires, Arnab Bhattacharyya, David Sontag

NeurIPS 2025 Product Distribution Learning with Imperfect Advice Arnab Bhattacharyya, Davin Choo, Philips George John, Themis Gouleakis

ICMLW 2024 Learnability of Parameter-Bounded Bayes Nets Arnab Bhattacharyya, Davin Choo, Sutanu Gayen, Dimitrios Myrisiotis

ALT 2024 Learning Bounded-Degree Polytrees with Known Skeleton Davin Choo, Joy Qiping Yang, Arnab Bhattacharyya, Clément L Canonne

ICML 2024 Online Bipartite Matching with Imperfect Advice Davin Choo, Themistoklis Gouleakis, Chun Kai Ling, Arnab Bhattacharyya

AISTATS 2024 Optimal Estimation of Gaussian (poly)trees Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, Arnab Bhattacharyya

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

ICML 2023 Active Causal Structure Learning with Advice Davin Choo, Themistoklis Gouleakis, Arnab Bhattacharyya

AAAI 2023 Constraint Optimization over Semirings Aduri Pavan, Kuldeep S. Meel, N. V. Vinodchandran, Arnab Bhattacharyya

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

CLeaR 2023 On the Interventional Kullback-Leibler Divergence Jonas Bernhard Wildberger, Siyuan Guo, Arnab Bhattacharyya, Bernhard Schölkopf

AISTATS 2023 Sample Complexity of Distinguishing Cause from Effect Jayadev Acharya, Sourbh Bhadane, Arnab Bhattacharyya, Saravanan Kandasamy, Ziteng Sun

AISTATS 2022 Efficient Interventional Distribution Learning in the PAC Framework Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, Vedant Raval, Vinodchandran N. Variyam

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

NeurIPS 2022 An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects Thanh Vinh Vo, Arnab Bhattacharyya, Young Lee, Tze-Yun Leong

AAAI 2022 Identifiability of Linear AMP Chain Graph Models Yuhao Wang, Arnab Bhattacharyya

NeurIPS 2022 Independence Testing for Bounded Degree Bayesian Networks Arnab Bhattacharyya, Clément L Canonne, Qiping Yang

NeurIPS 2022 Verification and Search Algorithms for Causal DAGs Davin Choo, Kirankumar Shiragur, Arnab Bhattacharyya

AISTATS 2021 Efficient Statistics for Sparse Graphical Models from Truncated Samples Arnab Bhattacharyya, Rathin Desai, Sai Ganesh Nagarajan, Ioannis Panageas

ALT 2021 Testing Product Distributions: A Closer Look Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, N. V. Vinodchandran

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

ICML 2020 Learning and Sampling of Atomic Interventions from Observations Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, Ashwin Maran, Vinodchandran N. Variyam

AAAI 2019 Minimum Intervention Cover of a Causal Graph Saravanan Kandasamy, Arnab Bhattacharyya, Vasant G. Honavar

COLT 2018 Hardness of Learning Noisy Halfspaces Using Polynomial Thresholds Arnab Bhattacharyya, Suprovat Ghoshal, Rishi Saket

NeurIPS 2018 Learning and Testing Causal Models with Interventions Jayadev Acharya, Arnab Bhattacharyya, Constantinos Daskalakis, Saravanan Kandasamy

ICML 2018 Testing Sparsity over Known and Unknown Bases Siddharth Barman, Arnab Bhattacharyya, Suprovat Ghoshal