Giuseppe De Nicolao
Statistical Learning Theory · 1998–2014
A researcher in the Science corpus: 6 papers; trusts 22 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Statistical Learning Theory — 24 citations into it
- Artificial Intelligence — 18 citations into it; #22,667 of 35,345 by trust here, #20,377 by citations
- Statistics and Probability — 17 citations into it
- Probabilistic & Bayesian Methods — 13 citations into it; #1,158 of 1,525 by trust here, #1,176 by citations
- Control and Systems Engineering — 11 citations into it; #2,436 of 8,257 by trust here, #1,518 by citations
- Computational Theory and Mathematics — 9 citations into it
- Computer Vision and Pattern Recognition — 9 citations into it
- Mathematical Physics — 8 citations into it
Whom Giuseppe De Nicolao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Grace Wahba
- Steve Smale
- Christopher K. I. Williams
- Felipe Cucker
- Manfred Opper
- Manfred Morari
- Charles A. Micchelli
- Tomaso A. Poggio
- Vladimir Naumovich Vapnik
- Ding-Xuan Zhou
Who trusts Giuseppe De Nicolao overall
- Rolf Findeisen
- Giancarlo Ferrari-Trecate
- Yu Zhang
- Frank Allgöwer
- Tianshi Chen
- Luca Galbusera
- Luigi Chisci
- Qiang Yang
- Daniel Limón
- Victor M. Zavala
Papers from Giuseppe De Nicolao's perspective
Ordered by how strongly the researchers Giuseppe De Nicolao trusts endorse each paper. Same corpus, a different order for every researcher.
- Spline Models for Observational Data
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Theory of Reproducing Kernels.
- A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines
- Kernel Methods for Pattern Analysis
- Task Clustering and Gating for Bayesian Multitask Learning
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- On the mathematical foundations of learning
- Estimating the Dimension of a Model
Their papers
- Kernel methods in system identification, machine learning and function estimation: A survey (2014) — 776 citations
- Bayesian Online Multitask Learning of Gaussian Processes (2010) — 77 citations
- A new kernel-based approach for linear system identification (2010) — 496 citations
- A stabilizing model-based predictive control algorithm for nonlinear systems (2001) — 372 citations
- Stability and Robustness of Nonlinear Receding Horizon Control (2000) — 155 citations
- Mining biomedical time series by combining structural analysis and temporal abstractions (1998)