Demo Data from VLDB 2026 / reviewers in the wild / expert
Seong Cheol Jeong
dblp:393/2631
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—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 |
Optimization for machine learning · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Optimization for machine learning › stochastic optimization › adaptive gradient methods
adam convergence |
0.8 | 1 | 2024 | ADOPT: Modified Adam Can Converge with Any β2 with the Optimal Rate · NeurIPS 2024 |
Machine learning › Optimization for machine learning › stochastic optimization
adaptive gradient methods |
0.8 | 1 | 2024 | ADOPT: Modified Adam Can Converge with Any β2 with the Optimal Rate · NeurIPS 2024 |
Methods — techniques the papers use, named apart from their topics
convergence analysis · 0.8adam optimizer · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | ADOPT: Modified Adam Can Converge with Any β2 with the Optimal Rate
Shohei Taniguchi, Keno Harada, Gouki Minegishi, Yuta Oshima, Seong Cheol Jeong, Go Nagahara, Tomoshi Iiyama, Masahiro Suzuki 0001, Yusuke Iwasawa, Yutaka Matsuo |
NeurIPS | 5 |