Titlebook: Learning and Intelligent Optimization; 17th International C Meinolf Sellmann,Kevin Tierney Conference proceedings 2023 The Editor(s) (if ap

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使苦惱

,Fast and?Robust Constrained Optimization via?Evolutionary and?Quadratic Programming,erature and sequential quadratic programming approaches. The proposed method is evaluated on numerous constrained optimization tasks from simple low dimensional problems to high dimensional realistic trajectory optimization scenarios, and showcase that is able to outperform other evolutionary algori

Apraxia

Hierarchical Machine Unlearning,ing are still not widely used due to model applicability, usage overhead, etc. Based on this situation, we propose a novel hierarchical learning method, Hierarchical Machine Unlearning (HMU), with the known distribution of unlearning requests. Compared with previous methods, ours has better efficien

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Addictive

,Generative Models via?Optimal Transport and?Gaussian Processes,that, for a given input, it provides both a prediction and the associated uncertainty. Thus, the generative properties are, by design, guaranteed by sampling the generated element around the prediction and depending on the uncertainty. Results on both toy examples and a dataset of images are provide

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,Real-World Streaming Process Discovery from?Low-Level Event Data,tes (i.e., case, activity and timestamp) are known, and apply unscaled discovery techniques to produce control-flow process models. In this research, we propose an original approach we have designed and deployed to mine processes of businesses. It features fully streamed and real-time techniques to

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,Heuristics Selection with?ML in?CP Optimizer, of diverse benchmark problems that is used to evaluate and document CPO performance before each release. This work also addresses two methodological challenges: the ability of the trained predictive models to robustly generalize to the diverse set of problems that may be encountered in practice, an

Cabg318

,Model-Based Feature Selection for?Neural Networks: A Mixed-Integer Programming Approach,lly reduce the size of the input to .15% while maintaining a good classification accuracy. This allows us to design DNNs with significantly fewer connections, reducing computational effort and producing DNNs that are more robust towards adversarial attacks.