VLDB 2026 Research / reviewers in the wild / expert
Charles Fefferman
dblp:82/6245
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 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
2 papers |
Representation and self-supervised learning · 98% Trustworthy machine learning · 2% | |
| Theoretical computer science
1 paper |
Computational geometry · 50% Information theory · 50% |
Topics — the 5 heaviest of 6, 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 › manifold learning
manifold fitting |
0.3 | 1 | 2018 | Fitting a Putative Manifold to Noisy Data · COLT 2018 |
Machine learning › Representation and self-supervised learning › representation learning › dimensionality reduction
manifold learning |
0.3 | 1 | 2018 | Fitting a Putative Manifold to Noisy Data · COLT 2018 |
Computational geometry › geometric modeling and processing › point cloud analysis › geometric reconstruction
manifold reconstruction |
0.3 | 1 | 2018 | Fitting a Putative Manifold to Noisy Data · COLT 2018 |
Information theory
noisy observations |
0.3 | 1 | 2018 | Fitting a Putative Manifold to Noisy Data · COLT 2018 |
Machine learning › Trustworthy machine learning › privacy › privacy attack
network inversion |
0.0 | 1 | 1993 | Recovering a Feed-Forward Net From Its Output · NIPS 1993 |
Methods — techniques the papers use, named apart from their topics
reach estimation · 0.7hausdorff distance · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Fitting a Putative Manifold to Noisy DataabstractIn the present work, we give a solution to the following question from manifold learning. Suppose data belonging to a high dimensional Euclidean space is drawn independently, identically distributed from a measure supported on a low dimensional twice differentiable embedded manifold $M$, and corrupted by a small amount of gaussian noise. How can we produce a manifold $M’$ whose Hausdorff distance to $M$ is small and whose reach is not much smaller than the reach of $M$? Charles Fefferman, Sergei Ivanov 0001, Yaroslav Kurylev, Matti Lassas, Hariharan Narayanan 0001 |
COLT | 1 |
| 1993 | Recovering a Feed-Forward Net From Its Output
Charles Fefferman, Scott Markel |
NIPS | 1 |