Demo Data from VLDB 2026 / reviewers in the wild / expert
Stanislas Polu
dblp:45/4904
· DBLP profile ↗
4ranked-venue papers
1as first author
3since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 1 first-author · 3 since 2021Theory of computation · 1
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
3 papers |
Automated reasoning and model checking · 100% | |
| Artificial intelligence
2 papers |
Language models and text generation · 56% Knowledge representation and reasoning · 44% | |
| Software engineering, system software, and programming languages
1 paper |
Program synthesis and code generation · 100% |
Topics — the 4 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Automated reasoning and model checking › theorem proving
formal mathematics |
1.2 | 2 | 2023 | Formal Mathematics Statement Curriculum Learning · ICLR 2023 miniF2F: a cross-system benchmark for formal Olympiad-level mathematics · ICLR 2022 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › automated reasoning
theorem proving |
0.6 | 1 | 2022 | Proof Artifact Co-Training for Theorem Proving with Language Models · ICLR 2022 |
Natural language and speech › Language models and text generation › mathematical reasoning
formal theorem proving |
0.2 | 1 | 2022 | miniF2F: a cross-system benchmark for formal Olympiad-level mathematics · ICLR 2022 |
Automated reasoning and model checking › theorem proving
interactive theorem proving |
0.2 | 1 | 2022 | Proof Artifact Co-Training for Theorem Proving with Language Models · ICLR 2022 |
Methods — techniques the papers use, named apart from their topics
curriculum learning · 1.3proof assistant · 1.1language model · 1.1co-training · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Formal Mathematics Statement Curriculum Learning
Stanislas Polu, Jesse Michael Han, Kunhao Zheng, Mantas Baksys, Igor Babuschkin, Ilya Sutskever |
ICLR | 1 |
| 2022 | Proof Artifact Co-Training for Theorem Proving with Language Models
Jesse Michael Han, Jason Rute, Yuhuai Wu, Edward W. Ayers, Stanislas Polu |
ICLR | 5 |
| 2022 | miniF2F: a cross-system benchmark for formal Olympiad-level mathematics
Kunhao Zheng, Jesse Michael Han, Stanislas Polu |
ICLR | 3 |
| 2007 | Discrepancy-Sensitive Dynamic Fractional Cascading, Dominated Maxima Searching, and 2-d Nearest Neighbors in Any Minkowski Metric
Mikhail J. Atallah, Marina Blanton, Michael T. Goodrich, Stanislas Polu |
WADS | 4 |