155 results on '"Cervellera, Cristiano"'
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2. Ensemble Aggregation Approaches for Functional Optimization
3. Receding-Horizon Dynamic Optimization of Port-City Traffic Interactions Over Shared Urban Infrastructure
4. Voronoi Recursive Binary Trees for the Optimization of Nonlinear Functionals
5. An imitation learning approach for the control of a low-cost low-accuracy robotic arm for unstructured environments
6. Optimized ensemble value function approximation for dynamic programming
7. Copula-based scenario generation for urban traffic models
8. Gradient Boosting with Extreme Learning Machines for the Optimization of Nonlinear Functionals
9. A Receding Horizon Approach for Berth Allocation Based on Random Search Optimization
10. Voronoi tree models for distribution-preserving sampling and generation
11. A preliminary experiment combining marine robotics and citizenship engagement using imitation learning
12. Model Predictive Control of Port–City Traffic Interactions Over Shared Urban Infrastructure
13. Model Predictive Control of Port–City Traffic Interactions Over Shared Urban Infrastructure
14. An imitation learning approach for the control of a low-cost low-accuracy robotic arm for unstructured environments
15. Echo state network ensembles for surrogate models with an application to urban mobility
16. A Deterministic Learning Approach Based on Discrepancy
17. Functional Optimization Through Semilocal Approximate Minimization
18. Design, optimization and performance evaluation of a content distribution overlay for streaming
19. Efficient global maximum likelihood estimation through kernel methods
20. Deep Learning and Low-discrepancy Sampling for Surrogate Modeling with an Application to Urban Traffic Simulation
21. Policy Optimization for Berth Allocation Problems
22. Optimization of a peer-to-peer system for efficient content replication
23. Improving the variability of urban traffic microsimulation through the calibration of generative parameter models.
24. Improving the variability of urban traffic microsimulation through the calibration of generative parameter models
25. Optimization of an eMule-like modifier strategy
26. Deterministic learning for maximum-likelihood estimation through neural networks
27. Efficient sampling in approximate dynamic programming algorithms
28. Learning Robustly Stabilizing Explicit Model Predictive Controllers: A Non-Regular Sampling Approach
29. Design of asymptotic estimators: an approach based on neural networks and nonlinear programming
30. Neural network and regression spline value function approximations for stochastic dynamic programming
31. Design of a peer-to-peer system for optimized content replication
32. Optimization of a large-scale water reservoir network by stochastic dynamic programming with efficient state space discretization
33. MODEL-BASED FEEDBACK CONTROL OF CONTAINER HANDLING IN INTERMODAL TERMINALS
34. Deterministic Learning and an Application in Optimal Control
35. Learning Robustly Stabilizing Explicit Model Predictive Controllers: A Non-Regular Sampling Approach
36. Water reservoirs management under uncertainty by approximating networks and learning from data
37. A Deterministic Learning Approach Based on Discrepancy
38. An Improved Load Flow Method for MV Networks Based on LV Load Measurements and Estimations
39. Approximating Networks for the Solution of T-Stage Stochastic Optimal Control Problems
40. A Novel Approach for Sampling in Approximate Dynamic Programming Based on $F$ -Discrepancy
41. An Extreme Learning Machine Approach to Density Estimation Problems
42. Lattice point sets for state sampling in approximate dynamic programming
43. Uniform histograms for change detection in multivariate data
44. Distribution-Preserving Stratified Sampling for Learning Problems
45. $F$-Discrepancy for Efficient Sampling in Approximate Dynamic Programming
46. Low-Discrepancy Points for Deterministic Assignment of Hidden Weights in Extreme Learning Machines
47. A Successful Change from TNN to TNNLS and a Very Successful Year
48. Efficient use of Nadaraya-Watson models and low-discrepancy sequences for approximate dynamic programming
49. Lattice point sets for efficient kernel smoothing models
50. Local Linear Regression for Function Learning: An Analysis Based on Sample Discrepancy
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