東華大學圖書館 |

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Learning and intelligent optimization/ edited by Meinolf Sellmann, Kevin Tierney.
其他題名:	17th International Conference, LION 17, Nice, France, June 4-8, 2023 : revised selected papers /
其他題名:	LION 17
其他作者:	Sellmann, Meinolf.
團體作者:	LION (Conference)
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	xiv, 616 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Machine learning - Congresses. -
電子資源:	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 ;14286..

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:

576368
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 /
LDR:04789nmm a2200349 a 4500 001 2335289
003 DE-He213
005 20231024190759.0
006 m d
007 cr nn 008maaau
008 240402s2023 sz s 0 eng d
020 $a 9783031445057 $q (electronic bk.)
020 $a 9783031445040 $q (paper)
024 7 $a 10.1007/978-3-031-44505-7 $2 doi
035 $a 978-3-031-44505-7
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .L56 2023
072 7 $a PBKS $2 bicssc
072 7 $a MAT006000 $2 bisacsh
072 7 $a PBKS $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .L763 2023
111 2 $a LION (Conference) $n (17th : $d 2023 : $c Nice, France) $3 3667566
245 1 0 $a Learning and intelligent optimization $h [electronic resource] : $b 17th International Conference, LION 17, Nice, France, June 4-8, 2023 : revised selected papers / $c edited by Meinolf Sellmann, Kevin Tierney.
246 3 $a LION 17
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xiv, 616 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 14286
505 0 $a 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.
520 $a 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.
650 0 $a Machine learning $x Congresses. $3 576368
650 1 4 $a Computational Mathematics and Numerical Analysis. $3 891040
700 1 $a Sellmann, Meinolf. $3 3200775
700 1 $a Tierney, Kevin. $3 2156277
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in computer science ; $v 14286. $3 3667567
856 4 0 $u https://doi.org/10.1007/978-3-031-44505-7
950 $a Computer Science (SpringerNature-11645)