3.8 Proceedings Paper

A Theory of PAC Learnability of Partial Concept Classes

Related references

Note: Only part of the references are listed.
Article Computer Science, Hardware & Architecture

Near-optimal Sample Complexity Bounds for Robust Learning of Gaussian Mixtures via Compression Schemes

Hassan Ashtiani et al.

JOURNAL OF THE ACM (2020)

Article Multidisciplinary Sciences

Reconciling modern machine-learning practice and the classical bias-variance trade-off

Mikhail Belkin et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)

Article Computer Science, Information Systems

Near-Optimal Sample Compression for Nearest Neighbors

Lee-Ad Gottlieb et al.

IEEE TRANSACTIONS ON INFORMATION THEORY (2018)

Article Computer Science, Hardware & Architecture

Sample Compression Schemes for VC Classes

Shay Moran et al.

JOURNAL OF THE ACM (2016)

Proceedings Paper Computer Science, Theory & Methods

Lower Bounds for Clique vs. Independent Set

Mika Goos

2015 IEEE 56TH ANNUAL SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE (2015)

Proceedings Paper Computer Science, Theory & Methods

RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response

Ulfar Erlingsson et al.

CCS'14: PROCEEDINGS OF THE 21ST ACM CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (2014)

Article Computer Science, Theory & Methods

The Algorithmic Foundations of Differential Privacy

Cynthia Dwork et al.

FOUNDATIONS AND TRENDS IN THEORETICAL COMPUTER SCIENCE (2013)

Article Computer Science, Artificial Intelligence

PAC-Bayesian compression bounds on the prediction error of learning algorithms for classification

T Graepel et al.

MACHINE LEARNING (2005)