Demo Data from EDBT 2026 / reviewers in the wild / expert
Gianluca Finocchio
dblp:225/5268
· DBLP profile ↗
1ranked-venue papers
1as 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 first-author · 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 · 80% Learning theory · 20% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference
bayesian nonparametric model |
0.7 | 1 | 2023 | Posterior Contraction for Deep Gaussian Process Priors · J. Mach. Learn. Res. 2023 |
Machine learning › Probabilistic and Bayesian machine learning › stochastic processes › gaussian process › hierarchical gaussian process
deep gaussian process |
0.7 | 1 | 2023 | Posterior Contraction for Deep Gaussian Process Priors · J. Mach. Learn. Res. 2023 |
Machine learning › Probabilistic and Bayesian machine learning › stochastic processes › gaussian process
gaussian process prior |
0.7 | 1 | 2023 | Posterior Contraction for Deep Gaussian Process Priors · J. Mach. Learn. Res. 2023 |
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference › bayesian asymptotics
posterior contraction rates |
0.7 | 1 | 2023 | Posterior Contraction for Deep Gaussian Process Priors · J. Mach. Learn. Res. 2023 |
Machine learning › Learning theory
statistical learning theory |
0.7 | 1 | 2023 | Posterior Contraction for Deep Gaussian Process Priors · J. Mach. Learn. Res. 2023 |
Methods — techniques the papers use, named apart from their topics
nonparametric regression · 0.7minimax rates · 0.7composition assumption · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Posterior Contraction for Deep Gaussian Process PriorsabstractWe study posterior contraction rates for a class of deep Gaussian process priors in the nonparametric regression setting under a general composition assumption on the regression function. It is shown that the contraction rates can achieve the minimax convergence rate (up to log n factors), while being adaptive to the underlying structure and smoothness of the target function. The proposed framework extends the Bayesian nonparametric theory for Gaussian process priors. Gianluca Finocchio, Johannes Schmidt-Hieber |
J. Mach. Learn. Res. | 1 |