Akansha Dey

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dblp:231/7710 · DBLP profile ↗

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1ranked-venue papers

0as first author

0since 2021 · last 2018

—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 · 50% Efficient and distributed learning · 25% Kernel, tree and ensemble methods · 25%

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

Topic	Weight	Papers	Last	Evidence papers
Machine learning › Efficient and distributed learning active learning	0.3	1	2018	Learning from discriminative feature feedback · NeurIPS 2018
Machine learning › Kernel, tree and ensemble methods decision tree	0.3	1	2018	Learning from discriminative feature feedback · NeurIPS 2018
Machine learning › Learning theory › online learning mistake bounds	0.3	1	2018	Learning from discriminative feature feedback · NeurIPS 2018
Machine learning › Learning theory online learning	0.3	1	2018	Learning from discriminative feature feedback · NeurIPS 2018

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

discriminative feature feedback · 0.3

Year	Publication	Venue	Position
2018	Learning from discriminative feature feedback abstract We consider the problem of learning a multi-class classifier from labels as well as simple explanations that we call "discriminative features". We show that such explanations can be provided whenever the target concept is a decision tree, or more generally belongs to a particular subclass of DNF formulas. We present an efficient online algorithm for learning from such feedback and we give tight bounds on the number of mistakes made during the learning process. These bounds depend only on the size of the target concept and not on the overall number of available features, which could be infinite. We also demonstrate the learning procedure experimentally. Sanjoy Dasgupta, Akansha Dey, Nicholas Roberts, Sivan Sabato	NeurIPS	2