Demo Data from SIGIR 2026 / reviewers in the wild / expert
Yuichiro Oyabu
dblp:220/5703
· DBLP profile ↗
3ranked-venue papers
0as first author
2since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 3 · 2 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.
| Software engineering, system software, and programming languages
1 paper |
Program analysis · 50% Program verification · 50% | |
| Theoretical computer science
1 paper |
Logic in computer science · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program analysis › static analysis
probabilistic program analysis |
0.5 | 1 | 2021 | Ranking and Repulsing Supermartingales for Reachability in Randomized Programs · ACM Trans. Program. Lang. Syst. 2021 |
Logic in computer science › domain theory
fixed points |
0.5 | 1 | 2021 | Ranking and Repulsing Supermartingales for Reachability in Randomized Programs · ACM Trans. Program. Lang. Syst. 2021 |
Methods — techniques the papers use, named apart from their topics
template-based synthesis · 1.0experimental comparison · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Control-data separation and logical condition propagation for efficient inference on probabilistic programs
Ichiro Hasuo, Yuichiro Oyabu, Clovis Eberhart, Kohei Suenaga, Kenta Cho 0002, Shin-ya Katsumata |
J. Log. Algebraic Methods Program. | 2 |
| 2021 | Ranking and Repulsing Supermartingales for Reachability in Randomized ProgramsabstractComputing reachability probabilities is a fundamental problem in the analysis of randomized programs. This article aims at a comprehensive and comparative account of various martingale-based methods for over- and under-approximating reachability probabilities. Based on the existing works that stretch across different communities (formal verification, control theory, etc.), we offer a unifying account. In particular, we emphasize the role of order-theoretic fixed points—a classic topic in computer science—in the analysis of randomized programs. This leads us to two new martingale-based techniques, too. We also make an experimental comparison using our implementation of template-based synthesis algorithms for those martingales. Toru Takisaka, Yuichiro Oyabu, Natsuki Urabe, Ichiro Hasuo |
ACM Trans. Program. Lang. Syst. | 2 |
| 2018 | Ranking and Repulsing Supermartingales for Reachability in Probabilistic Programs
Toru Takisaka, Yuichiro Oyabu, Natsuki Urabe, Ichiro Hasuo |
ATVA | 2 |