Demo Data from VLDB 2026 / reviewers in the wild / expert
Noah Schutte
dblp:358/6969
· DBLP profile ↗
3ranked-venue papers
3as first author
3since 2021 · last 2024
0000-0001-8499-6578ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 3 first-author · 3 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-author · 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.
| Artificial intelligence
2 papers |
Trustworthy machine learning · 50% Optimization for machine learning · 33% Planning, search and constraint satisfaction · 17% | |
| Theoretical computer science
1 paper |
Mathematical optimization · 100% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Optimization for machine learning
decision-focused learning |
0.8 | 1 | 2024 | Robust Losses for Decision-Focused Learning · IJCAI 2024 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
decision making under uncertainty |
0.8 | 1 | 2024 | Optimization Under Epistemic Uncertainty Using Prediction · IJCAI 2024 |
Machine learning › Trustworthy machine learning › uncertainty estimation
epistemic uncertainty |
0.8 | 1 | 2024 | Optimization Under Epistemic Uncertainty Using Prediction · IJCAI 2024 |
Machine learning › Trustworthy machine learning › robustness › robust learning
robust loss functions |
0.8 | 1 | 2024 | Robust Losses for Decision-Focused Learning · IJCAI 2024 |
Machine learning › Trustworthy machine learning
robustness |
0.8 | 1 | 2024 | Robust Losses for Decision-Focused Learning · IJCAI 2024 |
Machine learning › Optimization for machine learning
robust optimization |
0.8 | 1 | 2024 | Robust Losses for Decision-Focused Learning · IJCAI 2024 |
Mathematical optimization
optimization under uncertainty |
0.8 | 1 | 2024 | Optimization Under Epistemic Uncertainty Using Prediction · IJCAI 2024 |
Methods — techniques the papers use, named apart from their topics
prediction · 1.5epistemic uncertainty modeling · 1.5robust loss · 0.8decision-focused learning · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Improving Metaheuristic Efficiency for Stochastic Optimization by Sequential Predictive Sampling
Noah Schutte, Krzysztof Postek, Neil Yorke-Smith |
CPAIOR (2) | 1 |
| 2024 | Optimization Under Epistemic Uncertainty Using Prediction
Noah Schutte |
IJCAI | 1 |
| 2024 | Robust Losses for Decision-Focused Learning
Noah Schutte, Krzysztof Postek, Neil Yorke-Smith |
IJCAI | 1 |