Demo Data from EDBT 2026 / reviewers in the wild / expert
Patrick Haluptzok
dblp:325/5538
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 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 |
Language models and text generation · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Program synthesis and code generation · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation
code generation |
0.7 | 1 | 2023 | Language Models Can Teach Themselves to Program Better · ICLR 2023 |
Program synthesis and code generation › neural program synthesis
LLM-based program synthesis |
0.7 | 1 | 2023 | Language Models Can Teach Themselves to Program Better · ICLR 2023 |
Methods — techniques the papers use, named apart from their topics
self-training · 1.3language model · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Language Models Can Teach Themselves to Program Better
Patrick Haluptzok, Matthew Bowers, Adam Tauman Kalai |
ICLR | 1 |