Search Results for author:
Found 7 papers, 5 papers with code
Estimating Koopman operators with sketching to provably learn large scale dynamical systems
Our empirical and theoretical analysis shows that the proposed estimators provide a sound and efficient way to learn large scale dynamical systems.
K-Planes: Explicit Radiance Fields in Space, Time, and Appearance
We introduce k-planes, a white-box model for radiance fields in arbitrary dimensions.
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Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot
In this work, we focus on the instance segmentation task and provide a comprehensive study of different techniques that allow adapting an object segmentation model in presence of novel objects or different domains.
Physics Informed Shallow Machine Learning for Wind Speed Prediction
We train supervised learning algorithms using the past history of wind to predict its value at a future time (horizon).
Multiclass learning with margin: exponential rates with no bias-variance trade-off
We study the behavior of error bounds for multiclass classification under suitable margin conditions.
Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression
Our analysis shows the benefit of the proposed approach, that we hence incorporate in a library for large scale kernel methods to derive adaptively tuned solutions.
Kernel methods through the roof: handling billions of points efficiently
Kernel methods provide an elegant and principled approach to nonparametric learning, but so far could hardly be used in large scale problems, since na\"ive implementations scale poorly with data size.
