Demo Data from VLDB 2026 / reviewers in the wild / expert
Dmitry Kamenetsky
dblp:16/6850
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2008
—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 |
Probabilistic and Bayesian machine learning · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Probabilistic and Bayesian machine learning › probabilistic inference
exact inference |
0.1 | 1 | 2008 | Efficient Exact Inference in Planar Ising Models · NIPS 2008 |
Machine learning › Probabilistic and Bayesian machine learning › structured models
graphical models |
0.1 | 1 | 2008 | Efficient Exact Inference in Planar Ising Models · NIPS 2008 |
Methods — techniques the papers use, named apart from their topics
perfect matching · 0.1dual graph · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | Efficient Exact Inference in Planar Ising ModelsabstractWe present polynomial-time algorithms for the exact computation of lowest- energy states, worst margin violators, partition functions, and marginals in binary undirected graphical models. Our approach provides an interesting alternative to the well-known graph cut paradigm in that it does not impose any submodularity constraints; instead we require planarity to establish a correspondence with perfect matchings in an expanded dual graph. Maximum-margin parameter estimation for a boundary detection task shows our approach to be efficient and effective. Nicol N. Schraudolph, Dmitry Kamenetsky |
NIPS | 2 |