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Spencer Greenberg

dblp:131/6693 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Learning theory · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Learning theory
generalization bounds		 0.412019
Hypothesis Set Stability and Generalization · NeurIPS 2019
Machine learning › Learning theory › generalization bounds
rademacher complexity		 0.412019
Hypothesis Set Stability and Generalization · NeurIPS 2019
Machine learning › Learning theory › generalization bounds
transductive rademacher complexity		 0.412019
Hypothesis Set Stability and Generalization · NeurIPS 2019
Machine learning › Learning theory › generalization bounds
algorithmic stability		 0.112019
Hypothesis Set Stability and Generalization · NeurIPS 2019

Methods — techniques the papers use, named apart from their topics

rademacher complexity · 0.4hypothesis set stability · 0.4
YearPublicationVenuePosition
2019 Hypothesis Set Stability and Generalization
abstract
We present a study of generalization for data-dependent hypothesis sets. We give a general learning guarantee for data-dependent hypothesis sets based on a notion of transductive Rademacher complexity. Our main result is a generalization bound for data-dependent hypothesis sets expressed in terms of a notion of hypothesis set stability and a notion of Rademacher complexity for data-dependent hypothesis sets that we introduce. This bound admits as special cases both standard Rademacher complexity bounds and algorithm-dependent uniform stability bounds. We also illustrate the use of these learning bounds in the analysis of several scenarios.
Dylan J. Foster, Spencer Greenberg, Satyen Kale, Mehryar Mohri, Karthik Sridharan
 NeurIPS2