Research

Publications

2026

  1. Total Variation Distance Estimation through Domain Reduction
    Arnab Bhattacharyya, Graham Cormode, Yucheng Fu, and Kuldeep S. Meel
    CoRR, 2026
  2. Testing Sparse Functions over the Reals
    Vipul Arora, Arnab Bhattacharyya, Philips George John, and Sayantan Sen
    In ICALP, 2026

2025

  1. Computational Explorations of Total Variation Distance
    Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, Aduri Pavan, and N. V. Vinodchandran
    In ICLR, 2025
  2. Distribution Learning Meets Graph Structure Sampling
    Arnab Bhattacharyya, Sutanu Gayen, Philips George John, Sayantan Sen, and N. V. Vinodchandran
    In NeurIPS, 2025
  3. Learnability of Parameter-Bounded Bayes Nets
    Arnab Bhattacharyya, Davin Choo, Sutanu Gayen, and Dimitrios Myrisiotis
    In AAAI, 2025
  4. Learning High-dimensional Gaussians from Censored Data
    Arnab Bhattacharyya, Constantinos Daskalakis, Themis Gouleakis, and Yuhao Wang
    In AISTATS, 2025
  5. Learning multivariate Gaussians with imperfect advice
    Arnab Bhattacharyya, Davin Choo, Philips George John, and Themis Gouleakis
    In ICML, 2025
  6. Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set
    Davin Choo, Chandler Squires, Arnab Bhattacharyya, and David Sontag
    In CLeaR, 2025
  7. Product Distribution Learning with Imperfect Advice
    Arnab Bhattacharyya, Davin Choo, Philips George John, and Themis Gouleakis
    In NeurIPS, 2025
  8. Total variation distance for product distributions is #P-complete
    Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, A. Pavan, and N. V. Vinodchandran
    Information Processing Letters, 2025

2024

  1. Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs
    Philips George John, Arnab Bhattacharyya, Silviu Maniu, Dimitrios Myrisiotis, and Zhenan Wu
    CoRR, 2024
  2. Learning bounded-degree polytrees with known skeleton
    Davin Choo, Joy Qiping Yang, Arnab Bhattacharyya, and Clément L. Canonne
    In ALT, 2024
  3. Online bipartite matching with imperfect advice
    Davin Choo, Themistoklis Gouleakis, Chun Kai Ling, and Arnab Bhattacharyya
    In ICML, 2024
  4. Optimal estimation of Gaussian (poly)trees
    Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, and Arnab Bhattacharyya
    In AISTATS, 2024
  5. Outlier Robust Multivariate Polynomial Regression
    Vipul Arora, Arnab Bhattacharyya, Mathews Boban, Venkatesan Guruswami, and Esty Kelman
    In ESA, 2024
  6. Total Variation Distance Meets Probabilistic Inference
    Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, A. Pavan, and N. V. Vinodchandran
    In ICML, 2024

2023

  1. Active causal structure learning with advice
    Davin Choo, Themistoklis Gouleakis, and Arnab Bhattacharyya
    In ICML, 2023
  2. Constraint Optimization over Semirings
    Aduri Pavan, Kuldeep S. Meel, N. V. Vinodchandran, and Arnab Bhattacharyya
    In AAAI, 2023
  3. Low Degree Testing over the Reals
    Vipul Arora, Arnab Bhattacharyya, Noah Fleming, Esty Kelman, and Yuichi Yoshida
    In SODA, 2023
  4. Model Counting Meets F_0 Estimation
    A. Pavan, N. Variyam Vinodchandran, Arnab Bhattacharyya, and Kuldeep S. Meel
    ACM Trans. Database Syst., 2023
    Conference version: PODS 2021. Related research-highlight versions appeared in SIGMOD Record (2022) and Communications of the ACM (2023).
  5. Near-Optimal Degree Testing for Bayes Nets
    Vipul Arora, Arnab Bhattacharyya, Clément L. Canonne, and Joy Qiping Yang
    In ISIT, 2023
  6. Near-Optimal Learning of Tree-Structured Distributions by Chow and Liu
    Arnab Bhattacharyya, Sutanu Gayen, Eric Price, Vincent Y. F. Tan, and N. V. Vinodchandran
    SIAM J. Comput., 2023
    Conference version: STOC 2021. The arXiv link is to the earlier four-author version; the journal version adds Vincent Y. F. Tan.
  7. On Approximating Total Variation Distance
    Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, A. Pavan, and N. V. Vinodchandran
    In IJCAI, 2023
  8. On the Interventional Kullback-Leibler Divergence
    Jonas Bernhard Wildberger, Siyuan Guo, Arnab Bhattacharyya, and Bernhard Schölkopf
    In CLeaR, 2023
  9. Sample Complexity of Distinguishing Cause from Effect
    Jayadev Acharya, Sourbh Bhadane, Arnab Bhattacharyya, Saravanan Kandasamy, and Ziteng Sun
    In AISTATS, 2023

2022

  1. An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects
    Thanh Vinh Vo, Arnab Bhattacharyya, Young Lee, and Tze-Yun Leong
    In NeurIPS, 2022
  2. Efficient interventional distribution learning in the PAC framework
    Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, Vedant Raval, and N. Variyam Vinodchandran
    In AISTATS, 2022
  3. Identifiability of Linear AMP Chain Graph Models
    Yuhao Wang and Arnab Bhattacharyya
    In AISTATS, 2022
  4. Independence Testing for Bounded Degree Bayesian Networks
    Arnab Bhattacharyya, Clément L. Canonne, and Joy Qiping Yang
    In NeurIPS, 2022
  5. Learning Sparse Fixed-Structure Gaussian Bayesian Networks
    Arnab Bhattacharyya, Davin Choo, Rishikesh Gajjala, Sutanu Gayen, and Yuhao Wang
    In AISTATS, 2022
  6. Property Testing - Problems and Techniques
    Arnab Bhattacharyya and Yuichi Yoshida
    2022
  7. Universal 1-Bit Compressive Sensing for Bounded Dynamic Range Signals
    Sidhant Bansal, Arnab Bhattacharyya, Anamay Chaturvedi, and Jonathan Scarlett
    In ISIT, 2022
  8. Verification and search algorithms for causal DAGs
    Davin Choo, Kirankumar Shiragur, and Arnab Bhattacharyya
    In NeurIPS, 2022

2021

  1. A formal methods approach to predicting new features of the eukaryotic vesicle traffic system
    Arnab Bhattacharyya, Ashutosh Gupta, Lakshmanan Kuppusamy, Somya Mani, Ankit Shukla, Mandayam K. Srivas, and Mukund Thattai
    Acta Informatica, 2021
  2. Efficient Statistics for Sparse Graphical Models from Truncated Samples
    Arnab Bhattacharyya, Rathin Desai, Sai Ganesh Nagarajan, and Ioannis Panageas
    In AISTATS, 2021
  3. Parameterized Intractability of Even Set and Shortest Vector Problem
    Arnab Bhattacharyya, Bonnet, László Egri, Suprovat Ghoshal, Karthik C. S., Bingkai Lin, Pasin Manurangsi, and Dániel Marx
    Journal of the ACM, 2021
    Conference version: ICALP 2018. The combined journal article also incorporates a preliminary version presented at ESA 2016.
  4. Predicting winner and estimating margin of victory in elections using sampling
    Arnab Bhattacharyya and Palash Dey
    Artificial Intelligence, 2021
    Conference version: AAMAS 2015. The arXiv link is to the earlier winner-prediction paper, not the complete combined journal article. The journal article also incorporates work presented at IJCAI 2015.
  5. Testing Product Distributions: A Closer Look
    Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, and N. V. Vinodchandran
    In ALT, 2021

2020

  1. Combinatorial Lower Bounds for 3-Query LDCs
    Arnab Bhattacharyya, L. Sunil Chandran, and Suprovat Ghoshal
    In ITCS, 2020
  2. Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning
    Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, and N. V. Vinodchandran
    In NeurIPS, 2020
  3. Improved learning of k-parities
    Arnab Bhattacharyya, Ameet Gadekar, and Ninad Rajgopal
    Theor. Comput. Sci., 2020
    Conference version: COCOON 2018.
  4. Learning and Sampling of Atomic Interventions from Observations
    Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, Ashwin Maran, and N. Variyam Vinodchandran
    In ICML, 2020

2019

  1. An Optimal Algorithm for \ell_1-Heavy Hitters in Insertion Streams and Related Problems
    Arnab Bhattacharyya, Palash Dey, and David P. Woodruff
    ACM Trans. Algorithms, 2019
    Conference version: PODS 2016. Subsumes the earlier preprint Fishing out Winners from Vote Streams (arXiv:1508.04522).
  2. Average Bias and Polynomial Sources
    Arnab Bhattacharyya, Philips George John, Suprovat Ghoshal, and Raghu Meka
    Electron. Colloquium Comput. Complex., 2019
    The arXiv version was withdrawn by the authors in May 2019.
  3. Minimum Intervention Cover of a Causal Graph
    Saravanan Kandasamy, Arnab Bhattacharyya, and Vasant G. Honavar
    In AAAI, 2019

2018

  1. Hardness of Learning Noisy Halfspaces using Polynomial Thresholds
    Arnab Bhattacharyya, Suprovat Ghoshal, and Rishi Saket
    In COLT, 2018
  2. Learning and Testing Causal Models with Interventions
    Jayadev Acharya, Arnab Bhattacharyya, Constantinos Daskalakis, and Saravanan Kandasamy
    In NeurIPS, 2018
  3. Testing Sparsity over Known and Unknown Bases
    Siddharth Barman, Arnab Bhattacharyya, and Suprovat Ghoshal
    In ICML, 2018

2017

  1. Improved bounds for universal one-bit compressive sensing
    Jayadev Acharya, Arnab Bhattacharyya, and Pritish Kamath
    In ISIT, 2017
  2. Lower Bounds for 2-Query LCCs over Large Alphabet
    Arnab Bhattacharyya, Sivakanth Gopi, and Avishay Tal
    In RANDOM, 2017
  3. Lower Bounds for Constant Query Affine-Invariant LCCs and LTCs
    Arnab Bhattacharyya and Sivakanth Gopi
    ACM Trans. Comput. Theory, 2017
    Conference version: CCC 2016.
  4. On the Gap between Outcomes of Voting Rules
    Anurita Mathur and Arnab Bhattacharyya
    In AAMAS, 2017

2016

  1. On Higher-Order Fourier Analysis over Non-Prime Fields
    Arnab Bhattacharyya, Abhishek Bhowmick, and Chetan Gupta
    In RANDOM, 2016
    The arXiv link is to the earlier two-author version, Using higher-order Fourier analysis over general fields; the conference version adds Chetan Gupta.
  2. On the Hardness of Learning Sparse Parities
    Arnab Bhattacharyya, Ameet Gadekar, Suprovat Ghoshal, and Rishi Saket
    In ESA, 2016
  3. Tight lower bounds for linear 2-query LCCs over finite fields
    Arnab Bhattacharyya, Zeev Dvir, Shubhangi Saraf, and Amir Shpilka
    Combinatorica, 2016
    Conference version: FOCS 2011.

2015

  1. A unified framework for testing linear-invariant properties
    Arnab Bhattacharyya, Elena Grigorescu, and Asaf Shapira
    Random Struct. Algorithms, 2015
    Conference version: FOCS 2010.
  2. Algorithmic regularity for polynomials and applications
    Arnab Bhattacharyya, Pooya Hatami, and Madhur Tulsiani
    In Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2015, San Diego, CA, USA, January 4-6, 2015, 2015
  3. How friends and non-determinism affect opinion dynamics
    Arnab Bhattacharyya and Kirankumar Shiragur
    In CDC, 2015
  4. Lower bounds for testing triangle-freeness in Boolean functions
    Arnab Bhattacharyya and Ning Xie
    Computational Complexity, 2015
    Conference version: SODA 2010.

2014

  1. An explicit sparse recovery scheme in the L1-norm
    Arnab Bhattacharyya and Vineet Nair
    CoRR, 2014
  2. Polynomial Decompositions in Polynomial Time
    Arnab Bhattacharyya
    In ESA, 2014
  3. Steiner transitive-closure spanners of low-dimensional posets
    Piotr Berman, Arnab Bhattacharyya, Elena Grigorescu, Sofya Raskhodnikova, David P. Woodruff, and Grigory Yaroslavtsev
    Combinatorica, 2014
    Conference version: ICALP 2011.

2013

  1. A Bipartite Graph with Non-Unimodal Independent Set Sequence
    Arnab Bhattacharyya and Jeff Kahn
    Electron. J. Comb., 2013
  2. An Algebraic Characterization of Testable Boolean CSPs
    Arnab Bhattacharyya and Yuichi Yoshida
    In ICALP, 2013
  3. Approximation algorithms for spanner problems and Directed Steiner Forest
    Piotr Berman, Arnab Bhattacharyya, Konstantin Makarychev, Sofya Raskhodnikova, and Grigory Yaroslavtsev
    Inf. Comput., 2013
    Conference version: ICALP 2011. The arXiv link is to the earlier two-author version of Improved Approximation for the Directed Spanner Problem, not the complete combined journal article.
  4. Every locally characterized affine-invariant property is testable
    Arnab Bhattacharyya, Eldar Fischer, Hamed Hatami, Pooya Hatami, and Shachar Lovett
    In STOC, 2013
  5. Guest column: on testing affine-invariant properties over finite fields
    Arnab Bhattacharyya
    SIGACT News, 2013
  6. On the convergence of the Hegselmann-Krause system
    Arnab Bhattacharyya, Mark Braverman, Bernard Chazelle, and Huy L. Nguyen
    In ITCS, 2013
  7. Testing Low Complexity Affine-Invariant Properties
    Arnab Bhattacharyya, Eldar Fischer, and Shachar Lovett
    In SODA, 2013

2012

  1. Lower Bounds for Local Monotonicity Reconstruction from Transitive-Closure Spanners
    Arnab Bhattacharyya, Elena Grigorescu, Madhav Jha, Kyomin Jung, Sofya Raskhodnikova, and David P. Woodruff
    SIAM J. Discret. Math., 2012
    Conference version: RANDOM 2010.
  2. Testing Odd-Cycle-Freeness in Boolean Functions
    Arnab Bhattacharyya, Elena Grigorescu, Prasad Raghavendra, and Asaf Shapira
    Comb. Probab. Comput., 2012
    Conference version: SODA 2012.
  3. Testing Permanent Oracles - Revisited
    Sanjeev Arora, Arnab Bhattacharyya, Rajsekar Manokaran, and Sushant Sachdeva
    In RANDOM, 2012
  4. Transitive-Closure Spanners
    Arnab Bhattacharyya, Elena Grigorescu, Kyomin Jung, Sofya Raskhodnikova, and David P. Woodruff
    SIAM J. Comput., 2012
    Conference version: SODA 2009.

2011

  1. Testability of linear-invariant properties
    Arnab Bhattacharyya
    Massachusetts Institute of Technology, Cambridge, MA, USA, 2011
  2. Testing Linear-Invariant Non-Linear Properties
    Arnab Bhattacharyya, Victor Chen, Madhu Sudan, and Ning Xie
    Theory Comput., 2011
    Conference version: STACS 2009. A short-report version also appeared in Property Testing: Current Research and Surveys (2010).
  3. Testing monotonicity of distributions over general partial orders
    Arnab Bhattacharyya, Eldar Fischer, Ronitt Rubinfeld, and Paul Valiant
    In ICS, 2011
  4. The Complexity of Linear Dependence Problems in Vector Spaces
    Arnab Bhattacharyya, Piotr Indyk, David P. Woodruff, and Ning Xie
    In ICS, 2011

2010

  1. Optimal Testing of Reed-Muller Codes
    Arnab Bhattacharyya, Swastik Kopparty, Grant Schoenebeck, Madhu Sudan, and David Zuckerman
    In FOCS, 2010
    A chapter version also appeared in Property Testing: Current Research and Surveys (2010).
  2. Separations of Matroid Freeness Properties
    Arnab Bhattacharyya, Elena Grigorescu, Jakob Nordström, and Ning Xie
    CoRR, 2010

2009

  1. Robust Regulatory Networks
    Arnab Bhattacharyya and Bernhard Haeupler
    CoRR, 2009
  2. Transitive-Closure Spanners of the Hypercube and the Hypergrid
    Arnab Bhattacharyya, Elena Grigorescu, Kyomin Jung, Sofya Raskhodnikova, and David P. Woodruff
    Electron. Colloquium Comput. Complex., 2009

2008

  1. A Note on the Distance to Monotonicity of Boolean Functions
    Arnab Bhattacharyya
    Electron. Colloquium Comput. Complex., 2008

2006

  1. Morphogenesis as an amorphous computation
    Arnab Bhattacharyya
    In Third Conference on Computing Frontiers, 2006

2004

  1. Smell detection for eclipse
    Arnab Bhattacharyya and Robert M. Fuhrer
    In Companion to the OOPSLA, 2004

Travels

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