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Hrag Ohannessian

dblp:182/8939 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—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 · 69% Probabilistic and Bayesian machine learning · 21% Kernel, tree and ensemble methods · 10%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Learning theory
sample complexity		 0.212016
The Teaching Dimension of Linear Learners · ICML 2016
Machine learning › Learning theory › computational learning theory › machine teaching
teaching dimension		 0.212016
The Teaching Dimension of Linear Learners · ICML 2016
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › regression
linear regression		 0.112016
The Teaching Dimension of Linear Learners · ICML 2016
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › regression › least squares regression
ridge regression		 0.112016
The Teaching Dimension of Linear Learners · ICML 2016
Machine learning › Kernel, tree and ensemble methods
support vector machine		 0.112016
The Teaching Dimension of Linear Learners · ICML 2016

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

optimization-based learning · 0.2
YearPublicationVenuePosition
2016 The Teaching Dimension of Linear Learners
abstract
Teaching dimension is a learning theoretic quantity that specifies the minimum training set size to teach a target model to a learner. Previous studies on teaching dimension focused on version-space learners which maintain all hypotheses consistent with the training data, and cannot be applied to modern machine learners which select a specific hypothesis via optimization. This paper presents the first known teaching dimension for ridge regression, support vector machines, and logistic regression. We also exhibit optimal training sets that match these teaching dimensions. Our approach generalizes to other linear learners.
Xiaojin Zhu 0001, Hrag Ohannessian
 ICML3