VLDB 2026 Research / reviewers in the wild / expert
Joshua Haddad
dblp:313/3249
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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.
| Artificial intelligence
1 paper |
Optimization for machine learning · 50% Deep learning architectures and training · 50% | |
| Theoretical computer science
1 paper |
Mathematical optimization · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Deep learning architectures and training › scientific machine learning
neural surrogate model |
0.6 | 1 | 2022 | OMLT: Optimization & Machine Learning Toolkit · J. Mach. Learn. Res. 2022 |
Machine learning › Optimization for machine learning › model-based optimization › bayesian optimization
surrogate model |
0.6 | 1 | 2022 | OMLT: Optimization & Machine Learning Toolkit · J. Mach. Learn. Res. 2022 |
Mathematical optimization › integer programming
mixed-integer optimization |
0.6 | 1 | 2022 | OMLT: Optimization & Machine Learning Toolkit · J. Mach. Learn. Res. 2022 |
Mathematical optimization › black-box optimization
surrogate-based optimization |
0.6 | 1 | 2022 | OMLT: Optimization & Machine Learning Toolkit · J. Mach. Learn. Res. 2022 |
Methods — techniques the papers use, named apart from their topics
gradient boosted trees · 1.1algebraic modeling · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | OMLT: Optimization & Machine Learning ToolkitabstractThe optimization and machine learning toolkit (OMLT) is an open-source software package incorporating neural network and gradient-boosted tree surrogate models, which have been trained using machine learning, into larger optimization problems. We discuss the advances in optimization technology that made OMLT possible and show how OMLT seamlessly integrates with the algebraic modeling language Pyomo. We demonstrate how to use OMLT for solving decision-making problems in both computer science and engineering. Francesco Ceccon, Jordan Jalving, Joshua Haddad, Alexander Thebelt, Calvin Tsay, Carl D. Laird, Ruth Misener |
J. Mach. Learn. Res. | 3 |