VLDB 2026 Research / reviewers in the wild / expert
Sandeep Yaramakala
dblp:50/92
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2005
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-author
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 · 50% Representation and self-supervised learning · 50% | |
| Databases, data mining, and information retrieval
1 paper |
Data mining · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Representation and self-supervised learning › representation learning › dimensionality reduction
feature selection |
0.1 | 1 | 2005 | Speculative Markov Blanket Discovery for Optimal Feature Selection · ICDM 2005 |
Machine learning › Probabilistic and Bayesian machine learning › causal inference › causal discovery
markov blanket discovery |
0.1 | 1 | 2005 | Speculative Markov Blanket Discovery for Optimal Feature Selection · ICDM 2005 |
Data mining
pattern mining |
0.0 | 1 | 2005 | Speculative Markov Blanket Discovery for Optimal Feature Selection · ICDM 2005 |
Methods — techniques the papers use, named apart from their topics
independence tests · 0.1heuristic search · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | Speculative Markov Blanket Discovery for Optimal Feature SelectionabstractIn this paper we address the problem of learning the Markov blanket of a quantity from data in an efficient manner Markov blanket discovery can be used in the feature selection problem to find an optimal set of features for classification tasks, and is a frequently-used preprocessing phase in data mining, especially for high-dimensional domains. Our contribution is a novel algorithm for the induction of Markov blankets from data, called Fast-IAMB, that employs a heuristic to quickly recover the Markov blanket. Empirical results show that Fast-IAMB performs in many cases faster and more reliably than existing algorithms without adversely affecting the accuracy of the recovered Markov blankets. Sandeep Yaramakala, Dimitris Margaritis 0001 |
ICDM | 1 |