Journal Papers: 

11) O.H.M Padilla and S. Chatterjee. Risk Bounds for Quantile Trend Filtering. Biometrika. To appear. Available at

10) S. Chatterjee and S. Goswami. Adaptive Estimation of Multivariate Piecewise Polynomials and Bounded Variation Functions by Optimal Decision Trees. Annals of Statistics. To appear. Available at

9) S. Chatterjee and S. Goswami. New Risk Bounds in 2D Total Variation Denoising. IEEE Transactions of Information Theory. DOI: 10.1109/TIT.2021.3059657. Available at

8) A. Guntuboyina, D. Lieu, S. Chatterjee and B. Sen. Adaptive risk bounds in univariate total variation denoising and trend filtering. Annals of Statistics, Vol 48, Pages 205-229. Available at

7) S. Chatterjee and S. Mukherjee. On Estimation in Tournaments and Graphs Under Monotonicity Constraints.IEEE Transactions on Information Theory. DOI: 10.1109/TIT.2019.2893911 Available at

6) Q. Han, T. Wang, S. Chatterjee and R. Samworth. Isotonic Regression in General Dimensions. Annals of Statistics. http://DOI: 10.1214/18-AOS1753. Available at

Here is a recorded talk about the above paper.

5) S. Chatterjee and J. Lafferty. Denoising Flows on Trees. IEEE Transactions on Information Theory Volume: 64, Issue: 3, March 2018 ). DOI10.1109/TIT.2017.2782369. Available at

4) S. Chatterjee and J. Lafferty. Adaptive Risk Bounds in Unimodal Regression. Bernoulli. DOI: 10.3150/16-BEJ922. Available at

3) S. Chatterjee. An Improved Global Risk Bound in Concave Regression. Electronic Journal of Statistics 10.1 (2016): 1608-1629. Available at

2) S. Chatterjee, A. Guntuboyina and B. Sen. Bernoulli. Bernoulli. DOI: 10.3150/16-BEJ865. On matrix estimation under monotonicity constraints. Available at

1) S. Chatterjee, A. Guntuboyina, and B. Sen. On risk bounds in isotonic and other shape restricted regression problems. Annals of Statistics. vol. 43, pages 1774-1800. Available at

Peer Reviewed Conference Papers:

3) M. Bonakdarpour, S. Chatterjee, R. Barber, J. Lafferty. 35th International Conference on Machine Learning (ICML 2018). Available at arXiv:1805.06439.

2) Y. Zhu, S. Chatterjee, J. Duchi and J. Lafferty. Local minimax complexity of stochastic convex optimization. Advances in Neural Information Processing Systems 29, 2016. Available at

1) S. Chatterjee and A. Barron. Information theoretic validity of Penalized Likelihood. In 2014 IEEE International Symposium on Information Theory, ISIT 2014 (pp. 3027-3031). Available at


Preprints/Under Preparation Articles

  1. S. Chatterjee and S. Sen. Regret Minimization in Isotonic, Heavy Tailed Contextual Bandits via Adaptive Confidence Bands.
  2. Y.Yu and S. Chatterjee. Localizing Change Points in Piecewise Polynomials of General Degrees. Available at
  3. O.H.M Padilla and S. Chatterjee. Quantile Regression by Dyadic CART. Available at