Zobrazeno 1 - 10
of 605
pro vyhledávání: '"Cai, T. Tony"'
This paper considers minimax and adaptive transfer learning for nonparametric classification under the posterior drift model with distributed differential privacy constraints. Our study is conducted within a heterogeneous framework, encompassing dive
Externí odkaz:
http://arxiv.org/abs/2406.20088
This paper studies federated learning for nonparametric regression in the context of distributed samples across different servers, each adhering to distinct differential privacy constraints. The setting we consider is heterogeneous, encompassing both
Externí odkaz:
http://arxiv.org/abs/2406.06755
Federated learning has attracted significant recent attention due to its applicability across a wide range of settings where data is collected and analyzed across disparate locations. In this paper, we study federated nonparametric goodness-of-fit te
Externí odkaz:
http://arxiv.org/abs/2406.06749
This paper studies transfer learning for estimating the mean of random functions based on discretely sampled data, where, in addition to observations from the target distribution, auxiliary samples from similar but distinct source distributions are a
Externí odkaz:
http://arxiv.org/abs/2401.12331
Autor:
Cai, T. Tony, Pu, Hongming
Transfer learning for nonparametric regression is considered. We first study the non-asymptotic minimax risk for this problem and develop a novel estimator called the confidence thresholding estimator, which is shown to achieve the minimax optimal ri
Externí odkaz:
http://arxiv.org/abs/2401.12272
Estimating a covariance matrix and its associated principal components is a fundamental problem in contemporary statistics. While optimal estimation procedures have been developed with well-understood properties, the increasing demand for privacy pre
Externí odkaz:
http://arxiv.org/abs/2401.03820
Publikováno v:
Ann. Statist. 52(1): 392-411 (February 2024)
Optimal estimation and inference for both the minimizer and minimum of a convex regression function under the white noise and nonparametric regression models are studied in a nonasymptotic local minimax framework, where the performance of a procedure
Externí odkaz:
http://arxiv.org/abs/2305.00164
Achieving optimal statistical performance while ensuring the privacy of personal data is a challenging yet crucial objective in modern data analysis. However, characterizing the optimality, particularly the minimax lower bound, under privacy constrai
Externí odkaz:
http://arxiv.org/abs/2303.07152
This paper presents a selective survey of recent developments in statistical inference and multiple testing for high-dimensional regression models, including linear and logistic regression. We examine the construction of confidence intervals and hypo
Externí odkaz:
http://arxiv.org/abs/2301.10392
Motivated by applications in text mining and discrete distribution inference, we investigate the testing for equality of probability mass functions of $K$ groups of high-dimensional multinomial distributions. A test statistic, which is shown to have
Externí odkaz:
http://arxiv.org/abs/2301.01381