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Learning and intelligent optimization = 17th International Conference, LION 17, Nice, France, June 4-8, 2023 : revised selected papers /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Learning and intelligent optimization/ edited by Meinolf Sellmann, Kevin Tierney.
Reminder of title:	17th International Conference, LION 17, Nice, France, June 4-8, 2023 : revised selected papers /
remainder title:	LION 17
other author:	Sellmann, Meinolf.
corporate name:	Workshop on the Preservation of Stability under Discretization
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xiv, 616 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Machine learning - Congresses. -
Online resource:	https://doi.org/10.1007/978-3-031-44505-7
ISBN:	9783031445057

Learning and intelligent optimization = 17th International Conference, LION 17, Nice, France, June 4-8, 2023 : revised selected papers /
Learning and intelligent optimization17th International Conference, LION 17, Nice, France, June 4-8, 2023 : revised selected papers /[electronic resource] :LION 17edited by Meinolf Sellmann, Kevin Tierney. - Cham :Springer International Publishing :2023. - xiv, 616 p. :ill., digital ;24 cm. - Lecture notes in computer science,142860302-9743 ;. - Lecture notes in computer science ;6140..

Anomaly Classification to Enable Self-Healing in Cyber Physical Systems using Process Mining -- Hyper-box Classification Model using Mathematical Programming -- A leak localization algorithm in water distribution networks using probabilistic leak representation and optimal transport distance -- Fast and Robust Constrained Optimization via Evolutionary and Quadratic Programming -- Bayesian Optimization for Function Compositions with Applications to Dynamic Pricing -- A Bayesian optimization algorithm for constrained simulation optimization problems with heteroscedastic noise -- Hierarchical Machine Unlearning -- Explaining the Behavior of Reinforcement Learning Agents using Explaining the Behavior of Reinforcement Learning Agents using -- Deep Randomized Networks for Fast Learning -- Generative models via Optimal Transport and Gaussian Processes -- Real-world streaming process discovery from low-level event data -- Robust Neural Network Approach to System Identification in the High-Noise Regime -- GPU for Monte Carlo Search -- Learning the Bias Weights for Generalized Nested Rollout Policy Adaptation -- Heuristics selection with ML in CP Optimizer -- Model-based feature selection for neural networks: A mixed-integer programming approach -- An Error-Based Measure for Concept Drift Detection and Characterization -- Predict, Tune and Optimize for Data-Driven Shift Scheduling with Uncertain Demands -- On Learning When to Decompose Graphical Models -- Inverse Lighting with Differentiable Physically-Based Model -- Repositioning Fleet Vehicles: a Learning Pipeline -- Bayesian Decision Trees Inspired from Evolutionary Algorithms -- Towards Tackling MaxSAT by Combining Nested Monte Carlo with Local Search -- Relational Graph Attention-based Deep Reinforcement Learning: An Application to Flexible Job Shop Scheduling with Sequence-dependent Setup Times -- Experimental Digital Twin for Job Shops with Transportation Agents -- Learning to Prune Electric Vehicle Routing Problems -- A matheuristic approach for electric bus fleet scheduling -- Class GP: Gaussian Process Modeling for Heterogeneous Functions -- Surrogate Membership for Inferred Metrics in Fairness Evaluation -- The BeMi Stardust: a Structured Ensemble of Binarized Neural Network -- Discovering explicit scale-up criteria in crisis response with decision mining -- Job Shop Scheduling via Deep Reinforcement Learning: a Sequence to Sequence approach -- Generating a Graph Colouring Heuristic with Deep Q-Learning and Graph Neural Networks -- Multi-Task Predict-then-Optimize -- Integrating Hyperparameter Search into Model-Free AutoML with Context-Free Grammars -- Improving subtour elimination constraint generation in Branch-and-Cut algorithms for the TSP with Machine Learning -- Learn, Compare, Search: One Sawmill's Search for the Best Cutting Patterns Across And/or Trees -- Dynamic Police Patrol Scheduling with Multi-Agent Reinforcement Learning -- Analysis of Heuristics for Vector Scheduling and Vector Bin Packing -- Unleashing the potential of restart by detecting the search stagnation.

This book constitutes the refereed proceedings of the 17th International Conference on Learning and Intelligent Optimization, LION-17, held in Nice, France, during June 4-8, 2023. The 40 full papers presented have been carefully reviewed and selected from 83 submissions. They focus on all aspects of unleashing the potential of integrating machine learning and optimization approaches, including automatic heuristic selection, intelligent restart strategies, predict-then-optimize, Bayesian optimization, and learning to optimize.

ISBN: 9783031445057

Standard No.: 10.1007/978-3-031-44505-7doiSubjects--Topical Terms:

574552
Machine learning
--Congresses.

LC Class. No.: Q325.5 / .L56 2023

Dewey Class. No.: 006.31

Learning and intelligent optimization = 17th International Conference, LION 17, Nice, France, June 4-8, 2023 : revised selected papers /
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