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Laurent Saloff-Coste

dblp:72/1816 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 1998
—ORCID · none

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

Theory of computation · 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.

Theoretical computer science
1 paper
Algorithms and data structures · 67% Mathematical optimization · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithms and data structures › randomized algorithms › sampling
markov chain monte carlo		 0.011995
What do we know about the Metropolis algorithm? · STOC 1995
Mathematical optimization
metropolis algorithm		 0.011995
What do we know about the Metropolis algorithm? · STOC 1995
Algorithms and data structures › markov chains
mixing time		 0.011995
What do we know about the Metropolis algorithm? · STOC 1995

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

spectral gap bounds · 0.0markov chain analysis · 0.0
YearPublicationVenuePosition
1998 What Do We Know about the Metropolis Algorithm?
abstract
The Metropolis algorithm is a widely used procedure for sampling from a specified distribution on a large finite set. We survey what is rigorously known about running times. This includes work from statistical physics, computer science, probability, and statistics. Some new results (Propositions 6.1–6.5) are given as an illustration of the geometric theory of Markov chains.
Persi Diaconis, Laurent Saloff-Coste
 J. Comput. Syst. Sci.2
1995 What do we know about the Metropolis algorithm?
abstract
The Metropolis algorithm is a widely used procedure for sampling from a specified distribution on a large finite set.We survey what is rigorously known about running times.This includes work from statistical physics, computer science, probability and statistics.Some new results are given ae an illustration of the geometric theory of Markov chains.
Persi Diaconis, Laurent Saloff-Coste
 STOC2