18 citations · 67 across the 16 of their papers we have counts for
16 papers
Key Design Choices in Source-Free Unsupervised Domain Adaptation: An In-depth Empirical Analysis
Andrea Maracani, Raffaello Camoriano, Elisa Maiettini +3
This study provides a comprehensive benchmark framework for Source-Free Unsupervised Domain Adaptation (SF-UDA) in image classification, aiming to achieve a rigorous empirical unde…
Shortcuts for causal discovery of nonlinear models by score matching
Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco +1
The use of simulated data in the field of causal discovery is ubiquitous due to the scarcity of annotated real data. Recently, Reisach et al., 2021 highlighted the emergence of pat…
A Structured Prediction Approach for Robot Imitation Learning
Anqing Duan, Iason Batzianoulis, Raffaello Camoriano +3
We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observe…
Estimating Koopman operators with sketching to provably learn large scale dynamical systems
Giacomo Meanti, Antoine Chatalic, Vladimir R. Kostic +3
The theory of Koopman operators allows to deploy non-parametric machine learning algorithms to predict and analyze complex dynamical systems. Estimators such as principal component…
Regularization properties of dual subgradient flow
Vassilis Apidopoulos, Cesare Molinari, Lorenzo Rosasco +1
Dual gradient descent combined with early stopping represents an efficient alternative to the Tikhonov variational approach when the regularizer is strongly convex. However, for ma…
Scalable Causal Discovery with Score Matching
Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco +2
This paper demonstrates how to discover the whole causal graph from the second derivative of the log-likelihood in observational non-linear additive Gaussian noise models. Leveragi…