Liguria
Learning with SGD and Random Features
Luigi Carratino, Alessandro Rudi, Lorenzo Rosasco
Neural Information Processing Systems http://nips.cc/
- Europe > Italy > Lombardy > Milan (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- North America > Canada > Ontario > National Capital Region > Ottawa (0.04)
- Europe > Italy > Liguria > Genoa (0.04)
- Information Technology > Artificial Intelligence > Machine Learning (1.00)
- Information Technology > Data Science > Data Mining > Big Data (0.46)
- Europe > Italy > Lombardy > Milan (0.05)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- North America > Canada > Ontario > National Capital Region > Ottawa (0.04)
- Europe > Italy > Liguria > Genoa (0.04)
- Information Technology > Artificial Intelligence > Machine Learning (0.97)
- Information Technology > Data Science > Data Mining > Big Data (0.47)
Online-Within-Online Meta-Learning
Giulia Denevi, Dimitris Stamos, Carlo Ciliberto, Massimiliano Pontil
Whentheinputs associated Ex. 1), theterms
- Europe > United Kingdom > England > Greater London > London (0.05)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)
- Europe > Italy > Liguria > Genoa (0.04)
Learning To Learn Around A Common Mean
Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil
Neural Information Processing Systems http://nips.cc/
- Europe > United Kingdom > England > Greater London > London (0.05)
- North America > Canada > Quebec > Montreal (0.04)
- Europe > Italy > Liguria > Genoa (0.04)
Learning to Embed Distributions via Maximum Kernel Entropy
Empirical data can often be considered as samples from a set of probability distributions.
- North America > United States (0.14)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- Europe > Italy > Liguria > Genoa (0.04)
- (2 more...)
Bilevel learning of the Group Lasso structure
Jordan Frecon, Saverio Salzo, Massimiliano Pontil
Regression with group-sparsity penalty plays acentral role in high-dimensional predictionproblems.
- North America > United States (0.05)
- North America > Canada > Quebec > Montreal (0.05)
- Oceania > Australia > New South Wales > Sydney (0.04)
- (5 more...)
Statistical and Computational Trade-Offs in Kernel K-Means
Daniele Calandriello, Lorenzo Rosasco
More precisely, we study a Nyström approach to kernel k-means. Weanalyze thestatistical properties oftheproposed method andshow that it achieves the same accuracy of exact kernel k-means with only a fraction of computations.
- North America > United States > Massachusetts > Middlesex County > Cambridge (0.04)
- North America > Canada > Quebec > Montreal (0.04)
- Europe > Italy > Liguria > Genoa (0.04)
- Europe > United Kingdom > England > Oxfordshire > Oxford (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- Europe > Italy > Liguria > Genoa (0.04)
- North America > United States > California > San Francisco County > San Francisco (0.14)
- North America > United States > California > San Diego County > San Diego (0.04)
- North America > United States > Utah > Salt Lake County > Salt Lake City (0.04)
- (20 more...)