Concentration inequalities for statistical inference
H Zhang, SX Chen - ar** upper confidence bound
Abstract Upper Confidence Bound (UCB) method is arguably the most celebrated one used
in online decision making with partial information feedback. Existing techniques for …
in online decision making with partial information feedback. Existing techniques for …
Computational limits of a distributed algorithm for smoothing spline
In this paper, we explore statistical versus computational trade-off to address a basic
question in the application of a distributed algorithm: what is the minimal computational cost …
question in the application of a distributed algorithm: what is the minimal computational cost …
Frequentist coverage and sup-norm convergence rate in Gaussian process regression
Gaussian process (GP) regression is a powerful interpolation technique due to its flexibility
in capturing non-linearity. In this paper, we provide a general framework for understanding …
in capturing non-linearity. In this paper, we provide a general framework for understanding …
Growing-dimensional partially functional linear models: non-asymptotic optimal prediction error
H Zhang, X Lei - Physica Scripta, 2023 - iopscience.iop.org
Under the reproducing kernel Hilbert spaces (RKHS), we focus on the penalized least-
squares of the partially functional linear models (PFLM), whose predictor contains both …
squares of the partially functional linear models (PFLM), whose predictor contains both …
A theoretical framework of the scaled Gaussian stochastic process in prediction and calibration
Model calibration or data inversion is one of the fundamental tasks in uncertainty
quantification. In this work, we study the theoretical properties of the scaled Gaussian …
quantification. In this work, we study the theoretical properties of the scaled Gaussian …
Tight non-asymptotic inference via sub-Gaussian intrinsic moment norm
In non-asymptotic learning, variance-type parameters of sub-Gaussian distributions are of
paramount importance. However, directly estimating these parameters using the empirical …
paramount importance. However, directly estimating these parameters using the empirical …
A multiplier bootstrap approach to designing robust algorithms for contextual bandits
H **e, Q Tang, Q Zhu - IEEE Transactions on Neural Networks …, 2022 - ieeexplore.ieee.org
Upper confidence bound (UCB)-based contextual bandit algorithms require one to know the
tail property of the reward distribution. Unfortunately, such tail property is usually unknown or …
tail property of the reward distribution. Unfortunately, such tail property is usually unknown or …
Non-asymptotic optimal prediction error for growing-dimensional partially functional linear models
H Zhang, X Lei - arxiv preprint arxiv:2009.04729, 2020 - arxiv.org
Under the reproducing kernel Hilbert spaces (RKHS), we consider the penalized least-
squares of the partially functional linear models (PFLM), whose predictor contains both …
squares of the partially functional linear models (PFLM), whose predictor contains both …
How many machines can we use in parallel computing for kernel ridge regression?
This paper aims to solve a basic problem in distributed statistical inference: how many
machines can we use in parallel computing? In kernel ridge regression, we address this …
machines can we use in parallel computing? In kernel ridge regression, we address this …