1) Y.Yu, S. Chatterjee and H.Xu. Localizing Change Points in Piecewise Polynomials of General Degrees. To appear in Electronic Journal of Statistics.

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

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

4) S. Chatterjee and S. Goswami. New Risk Bounds in 2D Total Variation Denoising. IEEE Transactions of Information Theory, 2021

5) A. Guntuboyina, D. Lieu, S. Chatterjee and B. Sen. Spatial Adaptation in Trend Filtering. Annals of Statistics Vol. 48, p. 205 – 229, 2020.

6) S. Chatterjee and S. Mukherjee. On Estimation in Tournaments and Graphs Under Monotonicity Constraints IEEE Transactions on Information Theory, Vol. 65, Issue: 6, 2019

7) Q. Han, T. Wang, S. Chatterjee and R. Samworth. Isotonic Regression in General Dimensions. Annals of Statistics, Vol. 43, Number 5, p. 2440 – 2471, 2019.

Here is a recorded talk about the above paper.

8) S. Chatterjee and J. Lafferty. Adaptive Risk Bounds in Unimodal Regression, Bernoulli,
Vol. 25, Number 1, p. 1 – 25, 2019.

9) M. Bonakdarpour, S. Chatterjee, R. Barber, J. Lafferty. Prediction rule reshaping. 35th International Conference on Machine Learning (ICML 2018).

10) S. Chatterjee and J. Lafferty. Denoising Flows on Trees.  IEEE Transactions on Information Theory ( Volume: 64, Issue: 3, March 2018 )

11) S. Chatterjee, A. Guntuboyina and B. Sen. Bernoulli. Bernoulli. On Matrix Estimation Under
Monotonicity Constraints
, Vol. 24, p. 1072 – 1100, 2018.

12) Y. Zhu, S. Chatterjee, J. Duchi and J. Lafferty. Local minimax complexity of stochastic convex optimization. Advances in Neural Information Processing Systems (NeurIPS 2016), p. 3423 – 3431, 2016.

13) S. Chatterjee. An Improved Global Risk Bound in Concave RegressionElectronic Journal of Statistics 10.1 (2016): 1608-1629

14) 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, 2015

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


1) S. Chatterjee and S. Sen. Regret Minimization in Isotonic, Heavy Tailed Contextual Bandits via Adaptive Confidence Bands.

2) O.H.M Padilla and S. Chatterjee. Quantile Regression by Dyadic CART.