Active and Adaptive Sequential Learning with per Time-step Excess Risk Guarantees

Yuheng Bu, Jiaxun Lu, Venugopal V. Veeravalli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider solving a sequence of machine learning problems that vary in a bounded manner from one time-step to the next. To solve these problems in an accurate and data-efficient way, we propose an active and adaptive learning framework, in which we actively query the labels of the most informative samples from an unlabeled data pool, and adapt to the change by utilizing the information acquired in the previous steps. Our goal is to satisfy a pre-specified bound on the excess risk at each time-step. We first design the active querying algorithm by minimizing the excess risk using stochastic gradient descent in the maximum likelihood estimation setting. Then, we propose a sample size selection rule that minimizes the number of samples by adapting to the change in the learning problems, while satisfying the required bound on excess risk at each time-step. Based on the actively queried samples, we construct an estimator for the change in the learning problems, which we prove to be an asymptotically tight upper bound of its true value. We validate our algorithm and theory through experiments with real data.

Original languageEnglish (US)
Title of host publicationConference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1606-1610
Number of pages5
ISBN (Electronic)9781728143002
DOIs
StatePublished - Nov 2019
Event53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States
Duration: Nov 3 2019Nov 6 2019

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2019-November
ISSN (Print)1058-6393

Conference

Conference53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
CountryUnited States
CityPacific Grove
Period11/3/1911/6/19

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'Active and Adaptive Sequential Learning with per Time-step Excess Risk Guarantees'. Together they form a unique fingerprint.

Cite this