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Steven Ehrlich

dblp:27/10537 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none

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

Artificial intelligence and machine learning · 2

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 · 50% Learning paradigms · 25% Efficient and distributed learning · 25%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 100%
Theoretical computer science
1 paper
Algorithms and data structures · 100%

Topics — the 8 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Efficient and distributed learning
active learning		 0.212013
Efficient Semi-supervised and Active Learning of Disjunctions · ICML (1) 2013
Machine learning › Learning theory › PAC learning › agnostic learning
disjunction learning		 0.212013
Efficient Semi-supervised and Active Learning of Disjunctions · ICML (1) 2013
Machine learning › Learning theory
sample complexity		 0.212013
Efficient Semi-supervised and Active Learning of Disjunctions · ICML (1) 2013
Machine learning › Learning paradigms
semi-supervised learning		 0.212013
Efficient Semi-supervised and Active Learning of Disjunctions · ICML (1) 2013
Distributed systems › distributed system architecture › communication architecture
communication topology		 0.212013
Distributed k-means and k-median clustering on general communication topologies · NIPS 2013
Distributed systems
distributed algorithms		 0.212013
Distributed k-means and k-median clustering on general communication topologies · NIPS 2013
Algorithms and data structures
clustering		 0.212013
Distributed k-means and k-median clustering on general communication topologies · NIPS 2013
Algorithms and data structures › clustering
distributed clustering		 0.212013
Distributed k-means and k-median clustering on general communication topologies · NIPS 2013

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

k-median · 0.3k-means · 0.3distributed optimization · 0.3semi-supervised learning · 0.2random classification noise · 0.2active learning · 0.2
YearPublicationVenuePosition
2013 Efficient Semi-supervised and Active Learning of Disjunctions
abstract
We provide efficient algorithms for learning disjunctions in the semi-supervised setting under a natural regularity assumption introduced by (Balcan & Blum, 2005). We prove bounds on the sample complexity of our algorithms under a mild restriction on the data distribution. We also give an active learning algorithm with improved sample complexity and extend all our algorithms to the random classification noise setting.
Maria-Florina Balcan, Christopher Berlind, Steven Ehrlich, Yingyu Liang
 ICML (1)3
2013 Distributed k-means and k-median clustering on general communication topologies
Maria-Florina Balcan, Steven Ehrlich, Yingyu Liang
 NIPS2