Active Learning For Optimization
Active Learning for Optimization is a simulation-based optimization strategy that iteratively selects informative queries to guide the search for optimal solutions, distinguishing itself by balancing exploration and exploitation through uncertainty-aware sampling to reduce computational effort in expensive black-box optimization.
306
Publications
23.9K
Citations
1K
Authors
391
Institutions
Publications per year
2017–2026
188
306
Accelerated discovery of CO2 electrocatalysts using active machine learning
Miao Zhong, Kevin Tran, Yimeng Min et al. · Nature · 2020 · 1.5K citations
Natural Sciences, Chemical Engineering, Computer Science +16
Improving Generalization with Active Learning
David Cohn, Les Atlas, Richard E. Ladner · Machine Learning · 1994 · 1.3K citations · Full text
Information-Based Objective Functions for Active Data Selection
David Mackay · Neural Computation · 1992 · 1.3K citations
1–5 of 306
1K
6
h-index
4
h-index
4
h-index
4
h-index
3
h-index
1–5 of 1K
391
Massachusetts Institute of Technology
Cambridge, United States
10 h-index
25 publications
3K citations

Huazhong University of Science and Technology
Wuhan, China
8 h-index
33 publications
3.3K citations
ETH Zurich
Zurich, Switzerland
8 h-index
18 publications
1.6K citations

Centre National de la Recherche Scientifique
Paris, France
7 h-index
19 publications
766 citations
Argonne National Laboratory
Lemont, United States
7 h-index
25 publications
2.6K citations

Northwestern Polytechnical University
Xi'an, China
7 h-index
18 publications
1.4K citations
The University of Texas at Austin
Austin, United States
6 h-index
8 publications
433 citations
Microsoft (United States)
Redmond, United States
6 h-index
14 publications
595 citations
1–8 of 391
10 publications · 386 citations
10
h-index
Repository · 11 publications · 514 citations
10
h-index
9 publications · 351 citations
9
h-index
6 publications · 2.7K citations
6
h-index
6 publications · 190 citations
6
h-index
1–5