DeepAI AI Chat
Log In Sign Up

Transfer Learning for Nonparametric Classification: Minimax Rate and Adaptive Classifier

06/07/2019
by   T. Tony Cai, et al.
0

Human learners have the natural ability to use knowledge gained in one setting for learning in a different but related setting. This ability to transfer knowledge from one task to another is essential for effective learning. In this paper, we study transfer learning in the context of nonparametric classification based on observations from different distributions under the posterior drift model, which is a general framework and arises in many practical problems. We first establish the minimax rate of convergence and construct a rate-optimal two-sample weighted K-NN classifier. The results characterize precisely the contribution of the observations from the source distribution to the classification task under the target distribution. A data-driven adaptive classifier is then proposed and is shown to simultaneously attain within a logarithmic factor of the optimal rate over a large collection of parameter spaces. Simulation studies and real data applications are carried out where the numerical results further illustrate the theoretical analysis. Extensions to the case of multiple source distributions are also considered.

READ FULL TEXT
11/22/2022

Transfer Learning for Contextual Multi-armed Bandits

Motivated by a range of applications, we study in this paper the problem...
11/09/2020

A Computationally Efficient Classification Algorithm in Posterior Drift Model: Phase Transition and Minimax Adaptivity

In massive data analysis, training and testing data often come from very...
06/08/2021

Adaptive transfer learning

In transfer learning, we wish to make inference about a target populatio...
02/22/2023

Source-Function Weighted-Transfer Learning for Nonparametric Regression with Seemingly Similar Sources

The homogeneity, or more generally, the similarity between source domain...
04/09/2018

High-dimensional Linear Discriminant Analysis: Optimality, Adaptive Algorithm, and Missing Data

This paper aims to develop an optimality theory for linear discriminant ...
06/09/2015

Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective

Transfer learning assumes classifiers of similar tasks share certain par...
05/19/2023

Nonparametric classification with missing data

We introduce a new nonparametric framework for classification problems i...