We are interested in learning and optimization questions related to systems within the Natural Sciences. Our group is especially interested in Machine/Statistical Learning and Global Optimization aspects of experimental domains, that is, practical physical-, life- and agro-sciences search-problems whose simulated models are unavailable and thus require real-world experimentation.
Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study
Proceedings of the 25th International Conference on Pattern Recognition, ICPR2020 (2021) 10404–10411
On the Covariance-Hessian Relation in Evolution Strategies.
Theoretical Computer Science 801 (2020) 157—174
Benchmarking Discrete Optimization Heuristics with IOHprofiler.
Applied Soft Computing 88 (2020) 106027
Predict or Screen Your Expensive Assay? DoE vs. Surrogates in Experimental Combinatorial Optimization.
In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2019 ,2019, Ed. ,ACM Press New York, NY, USA, Pages 274?284
Research Group Leader
Prof. Ofer Shir