Demo Data from EDBT 2026 / reviewers in the wild / expert
Takeyuki Sasai
dblp:262/7196
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2025
0009-0001-3230-416XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Databases, data management, data science and information retrieval · 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.
| Theoretical computer science
1 paper |
Mathematical optimization · 67% Algorithms and data structures · 33% | |
| Artificial intelligence
1 paper |
Learning theory · 50% 3D vision · 50% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision
robust estimation |
0.9 | 1 | 2025 | Outlier Robust and Sparse Estimation of Linear Regression Coefficients · J. Mach. Learn. Res. 2025 |
Machine learning › Learning theory
statistical learning theory |
0.9 | 1 | 2025 | Outlier Robust and Sparse Estimation of Linear Regression Coefficients · J. Mach. Learn. Res. 2025 |
Algorithms and data structures › numerical linear algebra
linear regression |
0.9 | 1 | 2025 | Outlier Robust and Sparse Estimation of Linear Regression Coefficients · J. Mach. Learn. Res. 2025 |
Mathematical optimization › statistical estimation › high-dimensional estimation
sparse estimation |
0.9 | 1 | 2025 | Outlier Robust and Sparse Estimation of Linear Regression Coefficients · J. Mach. Learn. Res. 2025 |
Mathematical optimization
statistical estimation |
0.9 | 1 | 2025 | Outlier Robust and Sparse Estimation of Linear Regression Coefficients · J. Mach. Learn. Res. 2025 |
Methods — techniques the papers use, named apart from their topics
generic chaining · 1.7concentration inequalities · 1.7heavy-tailed distributions · 0.9heavy-tailed distribution · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Popularity‑Bias Vulnerability: Semi‑Supervised Label Inference Attack on Federated Recommender Systems
Kenji Shinoda, Takeyuki Sasai, Shintaro Fukushima |
RecSys | 2 |
| 2025 | Outlier Robust and Sparse Estimation of Linear Regression CoefficientsabstractWe consider outlier-robust and sparse estimation of linear regression coefficients, when the covariates and the noises are contaminated by adversarial outliers and noises are sampled from a heavy-tailed distribution. Our results present sharper error bounds under weaker assumptions than prior studies that share similar interests with this study. Our analysis relies on some sharp concentration inequalities resulting from generic chaining. Takeyuki Sasai, Hironori Fujisawa |
J. Mach. Learn. Res. | 1 |