Learning and Intelligent Optimization 17th International Conference, LION 17, Nice, France, June 4–8, 2023, Revised Selected Papers /
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...
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| Autres auteurs: | , |
| Format: | Électronique eBook |
| Langue: | anglais |
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Cham :
Springer International Publishing : Imprint: Springer,
2023.
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| Édition: | 1st ed. 2023. |
| Collection: | Lecture Notes in Computer Science,
14286 |
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| Accès en ligne: | https://doi.org/10.1007/978-3-031-44505-7 |
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Table des matières:
- 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 potentialof restart by detecting the search stagnation.