A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes

Steve Hanneke; Aryeh Kontorovich

A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes

Steve Hanneke, Aryeh Kontorovich

Department of Computer Science

Ben-Gurion University of the Negev

Research output: Contribution to journal › Conference article › peer-review

Abstract

We establish a tight characterization of the worst-case rates for the excess risk of agnostic learning with sample compression schemes and for uniform convergence for agnostic sample compression schemes.

Original language	American English
Pages (from-to)	489-505
Number of pages	17
Journal	Proceedings of Machine Learning Research
Volume	98
State	Published - 1 Jan 2019
Event	30th International Conference on Algorithmic Learning Theory, ALT 2019 - Chicago, United States Duration: 22 Mar 2019 → 24 Mar 2019 https://proceedings.mlr.press/v98

Keywords

Agnostic Learning
Compression Schemes
Lower Bounds
Uniform Convergence

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Software
Control and Systems Engineering
Statistics and Probability

Cite this

@article{cbb346c955354651afff718e1cb81d65,

title = "A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes",

abstract = "We establish a tight characterization of the worst-case rates for the excess risk of agnostic learning with sample compression schemes and for uniform convergence for agnostic sample compression schemes.",

keywords = "Agnostic Learning, Compression Schemes, Lower Bounds, Uniform Convergence",

author = "Steve Hanneke and Aryeh Kontorovich",

note = "Publisher Copyright: {\textcopyright} 2019 Proceedings of Machine Learning Research. All rights reserved.; 30th International Conference on Algorithmic Learning Theory, ALT 2019 ; Conference date: 22-03-2019 Through 24-03-2019",

year = "2019",

month = jan,

day = "1",

language = "American English",

volume = "98",

pages = "489--505",

journal = "Proceedings of Machine Learning Research",

issn = "2640-3498",

publisher = "ML Research Press",

url = "https://proceedings.mlr.press/v98",

}

TY - JOUR

T1 - A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes

AU - Hanneke, Steve

AU - Kontorovich, Aryeh

PY - 2019/1/1

Y1 - 2019/1/1

N2 - We establish a tight characterization of the worst-case rates for the excess risk of agnostic learning with sample compression schemes and for uniform convergence for agnostic sample compression schemes.

AB - We establish a tight characterization of the worst-case rates for the excess risk of agnostic learning with sample compression schemes and for uniform convergence for agnostic sample compression schemes.

KW - Agnostic Learning

KW - Compression Schemes

KW - Lower Bounds

KW - Uniform Convergence

UR - http://www.scopus.com/inward/record.url?scp=85161180683&partnerID=8YFLogxK

M3 - Conference article

SN - 2640-3498

VL - 98

SP - 489

EP - 505

JO - Proceedings of Machine Learning Research

JF - Proceedings of Machine Learning Research

T2 - 30th International Conference on Algorithmic Learning Theory, ALT 2019

Y2 - 22 March 2019 through 24 March 2019

ER -

A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes

Abstract

Keywords

All Science Journal Classification (ASJC) codes

Other files and links

Fingerprint

Cite this