Learning and Intelligent Optimization 17th International Conference, LION 17, Nice, France, June 4-8, 2023, Revised Selected Papers için kapak resmi
Başlık:
Learning and Intelligent Optimization 17th International Conference, LION 17, Nice, France, June 4-8, 2023, Revised Selected Papers
Yazar:
Sellmann, Meinolf. editor.
ISBN:
9783031445057
Basım Bilgisi:
1st ed. 2023.
Fiziksel Tanımlama:
XIV, 616 p. 172 illus., 142 illus. in color. online resource.
Seri:
Lecture Notes in Computer Science, 14286
İçerik:
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.
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