Demo Data from EDBT 2026 / reviewers in the wild / expert
Michelle Delcourt
dblp:121/1083
· DBLP profile ↗
3ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0003-1176-8931ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 3
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 · 50% Graph algorithms and graph theory · 33% Mathematical optimization · 17% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithms and data structures › randomized algorithms › sampling › markov chain monte carlo
glauber dynamics |
0.4 | 1 | 2019 | Improved Bounds for Randomly Sampling Colorings via Linear Programming · SODA 2019 |
Graph algorithms and graph theory
graph coloring |
0.4 | 1 | 2019 | Improved Bounds for Randomly Sampling Colorings via Linear Programming · SODA 2019 |
Mathematical optimization › linear programming
linear programming duality |
0.4 | 1 | 2019 | Improved Bounds for Randomly Sampling Colorings via Linear Programming · SODA 2019 |
Algorithms and data structures › randomized algorithms › sampling
markov chain monte carlo |
0.4 | 1 | 2019 | Improved Bounds for Randomly Sampling Colorings via Linear Programming · SODA 2019 |
Algorithms and data structures › randomized algorithms › sampling › markov chain monte carlo
rapid mixing |
0.4 | 1 | 2019 | Improved Bounds for Randomly Sampling Colorings via Linear Programming · SODA 2019 |
Graph algorithms and graph theory › graph coloring
sampling colorings |
0.4 | 1 | 2019 | Improved Bounds for Randomly Sampling Colorings via Linear Programming · SODA 2019 |
Methods — techniques the papers use, named apart from their topics
path coupling · 0.4linear programming duality · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Improved Bounds for Randomly Sampling Colorings via Linear ProgrammingabstractA well-known conjecture in computer science and statistical physics is that Glauber dynamics on the set of k-colorings of a graph G on n vertices with maximum degree Δ is rapidly mixing for k ≥ Δ + 2. In FOCS 1999, Vigoda [43] showed that the flip dynamics (and therefore also Glauber dynamics) is rapidly mixing for any . It turns out that there is a natural barrier at , below which there is no one-step coupling that is contractive with respect to the Hamming metric, even for the flip dynamics. We use linear programming and duality arguments to fully characterize the obstructions to going beyond . These extremal configurations turn out to be quite brittle, and in this paper we use this to give two proofs that the Glauber dynamics is rapidly mixing for any for some absolute constant ε0 > 0. This is the first improvement to Vigoda's result that holds for general graphs. Our first approach analyzes a variable-length coupling in which these configurations break apart with high probability before the coupling terminates, and our other approach analyzes a one-step path coupling with a new metric that counts the extremal configurations. Additionally, our results extend to list coloring, a widely studied generalization of coloring, where the previously best known results required k > 2Δ. Sitan Chen, Michelle Delcourt, Ankur Moitra, Guillem Perarnau, Luke Postle |
SODA | 2 |
| 2016 | Rainbow copies of C4 in edge-colored hypercubes
József Balogh, Michelle Delcourt, Bernard Lidický, Cory Palmer |
Discret. Appl. Math. | 2 |
| 2012 | Locating a robber on a graph via distance queries
James M. Carraher, Ilkyoo Choi, Michelle Delcourt, Lawrence H. Erickson, Douglas B. West |
Theor. Comput. Sci. | 3 |