1 citations · 1 across the 6 of their papers we have counts for
6 papers
From Isotonic to Lipschitz Regression: A New Interpolative Perspective on Shape-restricted Estimation
Kenta Takatsu, Tianyu Zhang, Arun Kumar Kuchibhotla
This manuscript bridges nonparametric smoothness-based and shape-restricted estimation, which may appear as two disjoint paradigms in the field. The proposed approach is motivated…
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch
Tianyu Zhang, Amin Banitalebi-Dehkordi, Yong Zhang
Branch-and-bound is a systematic enumerative method for combinatorial optimization, where the performance highly relies on the variable selection strategy. State-of-the-art handcra…
Regression in Tensor Product Spaces by the Method of Sieves
Tianyu Zhang, Noah Simon
Estimation of a conditional mean (linking a set of features to an outcome of interest) is a fundamental statistical task. While there is an appeal to flexible nonparametric procedu…
On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure
Yunhua Xiang, Tianyu Zhang, Xu Wang +2
Originally developed for imputing missing entries in low rank, or approximately low rank matrices, matrix completion has proven widely effective in many problems where there is no…
A Sieve Stochastic Gradient Descent Estimator for Online Nonparametric Regression in Sobolev ellipsoids
Tianyu Zhang, Noah Simon
The goal of regression is to recover an unknown underlying function that best links a set of predictors to an outcome from noisy observations. In nonparametric regression, one assu…
An Online Projection Estimator for Nonparametric Regression in Reproducing Kernel Hilbert Spaces
Tianyu Zhang, Noah Simon
The goal of nonparametric regression is to recover an underlying regression function from noisy observations, under the assumption that the regression function belongs to a pre-spe…