1. Exploring the Gap between Tolerant and Non-tolerant Distribution Testing [arxiv] [Journal version]
with Sourav Chakraborty, Eldar Fischer, Arijit Ghosh and Gopinath Mishra
IEEE Transactions on Information Theory, Volume: 71, Issue: 2, 2025.
2. Efficient Sample-optimal Learning of Gaussian Tree Models via Sample-optimal Testing of Gaussian Mutual Information [arxiv]
with Sutanu Gayen and Sanket Kale
To appear in Discover Data journal, Springer Nature. Initially accepted in the International Conference on Data Science and Management of Data (CODS-COMAD), 2024.
3. A (simple) classical algorithm for estimating Betti numbers [arxiv] [journal version]
with Simon Apers, Sander Gribling and Dániel Szabó
Quantum 1202, Volume 7, 2023.
Distribution Learning Meets Graph Structure Sampling [arxiv]
with Arnab Bhattacharyya, Sutanu Gayen, Philips George John and N.V. Vinodchandran
To appear in the Conference on Neural Information Processing Systems (NeurIPS), 2025.
Quantum property testing in sparse directed graphs [arxiv]
with Simon Apers, Frédéric Magniez and Dániel Szabó
International Conference on Randomization and Computation (RANDOM), 2025.
Testing (Conditional) Mutual Information [arxiv]
with Marco Tomamichel and Jan Seyfried
Conference on Learning Theory (COLT), 2025.
with Sourav Chakraborty, Eldar Fischer, Arijit Ghosh, Amit Levi and Gopinath Mishra
Symposium on Theory of Computing (STOC), 2025.
Featured in Oded Goldreich's Choices.
Settling the complexity of testing grainedness of distributions, and application to uniformity testing in the Huge Object model [eccc][conference version]
with Clément Canonne and Joy Qiping Yang
Innovations in Theoretical Computer Science (ITCS), 2025.
Featured in Oded Goldreich's Choices.
Testing Self-Reducible Samplers [arxiv] [conference version]
with Rishiraj Bhattacharyya, Sourav Chakraborty, Yash Pote and Uddalok Sarkar
AAAI Conference on Artificial Intelligence (AAAI), 2024.
Testing of Index-Invariant Properties in the Huge Object Model [arxiv] [eccc] [conference version]
with Sourav Chakraborty, Eldar Fischer, Arijit Ghosh and Gopinath Mishra
Conference on Learning Theory (COLT), 2023.
Featured in Oded Goldreich's Choices.
A (simple) classical algorithm for estimating Betti numbers [arxiv] [journal version]
with Simon Apers, Sander Gribling and Dániel Szabó
Quantum Computing Theory in Practice (QCTiP), 2023.
Full version accepted in Quantum 2023.
Testing of Horn Samplers [Conference version]
with Ansuman Banerjee, Shayak Chakraborty, Sourav Chakraborty, Kuldeep S. Meel and Uddalok Sarkar
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.
Exploring the Gap between Tolerant and Non-tolerant Distribution Testing [arxiv] [conference version]
with Sourav Chakraborty, Eldar Fischer, Arijit Ghosh and Gopinath Mishra
International Conference on Randomization and Computation (RANDOM), 2022. [Conference Talk]
Highlights of Algorithms (HALG), 2023.
Full version accepted in IEEE Transactions on Information Theory.
Tolerant Bipartiteness Testing in Dense Graphs [arxiv] [conference version]
with Arijit Ghosh, Gopinath Mishra and Rahul Raychaudhury
International Colloquium on Automata, Languages and Programming (ICALP), 2022.
Highlights of Algorithms (HALG), 2023.
Interplay between Graph Isomorphism and Earth Mover’s Distance in the Query and Communication Worlds [eccc][conference version]
with Sourav Chakraborty, Arijit Ghosh and Gopinath Mishra
International Conference on Randomization and Computation (RANDOM), 2021. [Conference Talk]
Highlights of Algorithms (HALG), 2022.
Near Uniform Triangle Sampling Over Adjacency List Graph Streams [arxiv]
with Arijit Bishnu, Arijit Ghosh and Gopinath Mishra