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VLDB 2026 Research / reviewers in the wild / expert

Malay Ghosh

dblp:23/8506 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 since 2021

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 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › statistical inference
bayesian inference		 0.712023
Posterior Consistency for Bayesian Relevance Vector Machines · J. Mach. Learn. Res. 2023
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference › bayesian nonparametric model
bayesian nonparametric regression		 0.712023
Posterior Consistency for Bayesian Relevance Vector Machines · J. Mach. Learn. Res. 2023
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference › bayesian asymptotics
posterior consistency		 0.712023
Posterior Consistency for Bayesian Relevance Vector Machines · J. Mach. Learn. Res. 2023
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference › bayesian asymptotics
posterior contraction rates		 0.712023
Posterior Consistency for Bayesian Relevance Vector Machines · J. Mach. Learn. Res. 2023
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference › sparse bayesian learning
relevance vector machine		 0.712023
Posterior Consistency for Bayesian Relevance Vector Machines · J. Mach. Learn. Res. 2023

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

reproducing kernel hilbert space · 0.7global-local priors · 0.7
YearPublicationVenuePosition
2023 Posterior Consistency for Bayesian Relevance Vector Machines
abstract
Statistical modeling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. Chakraborty et al. (2012) did a full hierarchical Bayesian analysis of nonlinear regression in such situations using relevance vector machines based on reproducing kernel Hilbert space (RKHS). But they did not provide any theoretical properties associated with their procedure. The present paper revisits their problem, introduces a new class of global-local priors different from theirs, and provides results on posterior consistency as well as on posterior contraction rates.
Malay Ghosh
 J. Mach. Learn. Res.2