141 citations · 222 across the 20 of their papers we have counts for
26 papers
An Exact Pointwise Characterization for Total Variation Denoising in Quantile Regression
Deep Ghoshal, Sabyasachi Chatterjee
Total variation denoising (TVD) is a classical method for denoising and curve fitting, yet an explicit pointwise description of its fitted values has only recently been established…
Central Limit Theorem for Gram-Schmidt Random Walk Design
Sabyasachi Chatterjee, Partha S. Dey, Subhajit Goswami
We prove a central limit theorem for the Horvitz-Thompson estimator based on the Gram-Schmidt Walk (GSW) design, recently developed in Harshaw et al.(2022). In particular, we consi…
Spatially Adaptive Online Prediction of Piecewise Regular Functions
Sabyasachi Chatterjee, Subhajit Goswami
We consider the problem of estimating piecewise regular functions in an online setting, i.e., the data arrive sequentially and at any round our task is to predict the value of the…
Element-wise Estimation Error of Generalized Fused Lasso
Teng Zhang, Sabyasachi Chatterjee
The main result of this article is that we obtain an elementwise error bound for the Fused Lasso estimator for any general convex loss function . We then focus on the special ca…
A Cross Validation Framework for Signal Denoising with Applications to Trend Filtering, Dyadic CART and Beyond
Anamitra Chaudhuri, Sabyasachi Chatterjee
This paper formulates a general cross validation framework for signal denoising. The general framework is then applied to nonparametric regression methods such as Trend Filtering a…
Regret Minimization in Isotonic, Heavy-Tailed Contextual Bandits via Adaptive Confidence Bands
Sabyasachi Chatterjee, Subhabrata Sen
In this paper we initiate a study of non parametric contextual bandits under shape constraints on the mean reward function. Specifically, we study a setting where the context is on…
Quantile Regression by Dyadic CART
Oscar Hernan Madrid Padilla, Sabyasachi Chatterjee
In this paper we propose and study a version of the Dyadic Classification and Regression Trees (DCART) estimator from Donoho (1997) for (fixed design) quantile regression in genera…
A discrete dislocation dynamics study of precipitate bypass mechanisms in nickel-based superalloys
Sabyasachi Chatterjee, Yang Li, Giacomo Po
Order strengthening in nickel-based superalloys is associated with the extra stress required for dislocations to bypass the precipitates distributed in the matrix. A rich…
How useful are formal hierarchies? A case study on averaging dislocation dynamics to define meso-macro plasticity
Sabyasachi Chatterjee, Amit Acharya
A formal hierarchy of exact evolution equations are derived for physically relevant space-time averages of state functions of microscopic dislocation dynamics. While such hierarchi…
Localising change points in piecewise polynomials of general degrees
Yi Yu, Sabyasachi Chatterjee, Haotian Xu
In this paper we are concerned with a sequence of univariate random variables with piecewise polynomial means and independent sub-Gaussian noise. The underlying polynomials are all…
Risk Bounds for Quantile Trend Filtering
Oscar Hernan Madrid Padilla, Sabyasachi Chatterjee
We study quantile trend filtering, a recently proposed method for nonparametric quantile regression with the goal of generalizing existing risk bounds known for the usual trend fil…
Plasticity without phenomenology: a first step
Sabyasachi Chatterjee, Giacomo Po, Xiaohan Zhang +2
A novel, concurrent multiscale approach to meso/macroscale plasticity is demonstrated. It utilizes a carefully designed coupling of a partial differential equation (pde) based theo…
Adaptive Estimation of Multivariate Piecewise Polynomials and Bounded Variation Functions by Optimal Decision Trees
Sabyasachi Chatterjee, Subhajit Goswami
Proposed by Donoho (1997), Dyadic CART is a nonparametric regression method which computes a globally optimal dyadic decision tree and fits piecewise constant functions in two dime…
New Risk Bounds for 2D Total Variation Denoising
Sabyasachi Chatterjee, Subhajit Goswami
2D Total Variation Denoising (TVD) is a widely used technique for image denoising. It is also an important nonparametric regression method for estimating functions with heterogenou…
Prediction Rule Reshaping
Matt Bonakdarpour, Sabyasachi Chatterjee, Rina Foygel Barber +1
Two methods are proposed for high-dimensional shape-constrained regression and classification. These methods reshape pre-trained prediction rules to satisfy shape constraints like…
Isotonic regression in general dimensions
Qiyang Han, Tengyao Wang, Sabyasachi Chatterjee +1
We study the least squares regression function estimator over the class of real-valued functions on that are increasing in each coordinate. For uniformly bounded signals…
Computing Singularly Perturbed Differential Equations
Sabyasachi Chatterjee, Amit Acharya, Zvi Artstein
A computational tool for coarse-graining nonlinear systems of ordinary differential equations in time is discussed. Three illustrative model examples are worked out that demonstrat…
Adaptive Risk Bounds in Univariate Total Variation Denoising and Trend Filtering
Adityanand Guntuboyina, Donovan Lieu, Sabyasachi Chatterjee +1
We study trend filtering, a relatively recent method for univariate nonparametric regression. For a given positive integer , the -th order trend filtering estimator is define…
Local Minimax Complexity of Stochastic Convex Optimization
Yuancheng Zhu, Sabyasachi Chatterjee, John Duchi +1
We extend the traditional worst-case, minimax analysis of stochastic convex optimization by introducing a localized form of minimax complexity for individual functions. Our main re…
On Estimation in Tournaments and Graphs under Monotonicity Constraints
Sabyasachi Chatterjee, Sumit Mukherjee
We consider the problem of estimating the probability matrix governing a tournament or linkage in graphs from incomplete observations, under the assumption that the probability mat…
Denoising Flows on Trees
Sabyasachi Chatterjee, John Lafferty
We study the estimation of flows on trees, a structured generalization of isotonic regression. A tree flow is defined recursively as a positive flow value into a node that is parti…
An Improved Global Risk Bound in Concave Regression
Sabyasachi Chatterjee
A new risk bound is presented for the problem of convex/concave function estimation, using the least squares estimator. The best known risk bound, as had appeared in \citet{GSvex},…
Adaptive Risk Bounds in Unimodal Regression
Sabyasachi Chatterjee, John Lafferty
We study the statistical properties of the least squares estimator in unimodal sequence estimation. Although closely related to isotonic regression, unimodal regression has not bee…
On matrix estimation under monotonicity constraints
Sabyasachi Chatterjee, Adityanand Guntuboyina, Bodhisattva Sen
We consider the problem of estimating an unknown matrix from noisy observations under the constraint that is nondecreasing in both rows…