109 citations · 235 across the 11 of their papers we have counts for
7 papers · 1 filter
On Sparsity Inducing Regularization Methods for Machine Learning
Andreas Argyriou, Luca Baldassarre, Charles A. Micchelli +1
During the past years there has been an explosion of interest in learning methods based on sparsity regularization. In this paper, we discuss a general class of such methods, in wh…
Group-Sparse Model Selection: Hardness and Relaxations
Luca Baldassarre, Nirav Bhan, Volkan Cevher +2
Group-based sparsity models are proven instrumental in linear regression problems for recovering signals from much fewer measurements than standard compressive sensing. The main pr…
Optimal Computational Trade-Off of Inexact Proximal Methods
Pierre Machart, Sandrine Anthoine, Luca Baldassarre
In this paper, we investigate the trade-off between convergence rate and computational cost when minimizing a composite functional with proximal-gradient methods, which are popular…
Modelling transition dynamics in MDPs with RKHS embeddings
Steffen Grunewalder, Guy Lever, Luca Baldassarre +2
We propose a new, nonparametric approach to learning and representing transition dynamics in Markov decision processes (MDPs), which can be combined easily with dynamic programming…
Conditional mean embeddings as regressors - supplementary
Steffen Grünewälder, Guy Lever, Luca Baldassarre +3
We demonstrate an equivalence between reproducing kernel Hilbert space (RKHS) embeddings of conditional distributions and vector-valued regressors. This connection introduces a nat…
Modeling transition dynamics in MDPs with RKHS embeddings of conditional distributions
Steffen Grünewälder, Luca Baldassarre, Massimiliano Pontil +2
We propose a new, nonparametric approach to estimating the value function in reinforcement learning. This approach makes use of a recently developed representation of conditional d…
A General Framework for Structured Sparsity via Proximal Optimization
Andreas Argyriou, Luca Baldassarre, Jean Morales +1
We study a generalized framework for structured sparsity. It extends the well-known methods of Lasso and Group Lasso by incorporating additional constraints on the variables as par…