Joshua Haddad

dblp:313/3249 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Machine learning › Deep learning architectures and training › scientific machine learning
neural surrogate model
0.612022
OMLT: Optimization & Machine Learning Toolkit · J. Mach. Learn. Res. 2022
Machine learning › Optimization for machine learning › model-based optimization › bayesian optimization
surrogate model
0.612022
OMLT: Optimization & Machine Learning Toolkit · J. Mach. Learn. Res. 2022
Mathematical optimization › integer programming
mixed-integer optimization
0.612022
OMLT: Optimization & Machine Learning Toolkit · J. Mach. Learn. Res. 2022
Mathematical optimization › black-box optimization
surrogate-based optimization
0.612022
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
YearPublicationVenuePosition
2022 OMLT: Optimization & Machine Learning Toolkit
abstract
The 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