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Elliott H. Lieb

dblp:61/6728 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none

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

Theory of computation · 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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Learning theory › loss function
bregman divergence		 0.112009
Probabilistic coherence and proper scoring rules · IEEE Trans. Inf. Theory 2009
Machine learning › Learning theory › loss function
proper scoring rules		 0.112009
Probabilistic coherence and proper scoring rules · IEEE Trans. Inf. Theory 2009
YearPublicationVenuePosition
2009 Probabilistic coherence and proper scoring rules
abstract
This paper provides self-contained proof of a theorem relating probabilistic coherence of forecasts to their non-domination by rival forecasts with respect to any proper scoring rule. The theorem recapitulates insights achieved by other investigators, and clarifies the connection of coherence and proper scoring rules to Bregman divergence.
Joel B. Predd, Robert Seiringer, Elliott H. Lieb, Daniel N. Osherson, H. Vincent Poor, Sanjeev R. Kulkarni
 IEEE Trans. Inf. Theory3