109 citations · 235 across the 11 of their papers we have counts for
12 papers
Convex block-sparse linear regression with expanders -- provably
Anastasios Kyrillidis, Bubacarr Bah, Rouzbeh Hasheminezhad +3
Sparse matrices are favorable objects in machine learning and optimization. When such matrices are used, in place of dense ones, the overall complexity requirements in optimization…
Learning-based Compressive Subsampling
Luca Baldassarre, Yen-Huan Li, Jonathan Scarlett +3
The problem of recovering a structured signal from a set of dimensionality-reduced linear measurements arises in…
Structured Sparsity: Discrete and Convex approaches
Anastasios Kyrillidis, Luca Baldassarre, Marwa El-Halabi +2
Compressive sensing (CS) exploits sparsity to recover sparse or compressible signals from dimensionality reducing, non-adaptive sensing mechanisms. Sparsity is also used to enhance…
Model-based Sketching and Recovery with Expanders
Bubacarr Bah, Luca Baldassarre, Volkan Cevher
Linear sketching and recovery of sparse vectors with randomly constructed sparse matrices has numerous applications in several areas, including compressive sensing, data stream com…
Convexity in source separation: Models, geometry, and algorithms
Michael B. McCoy, Volkan Cevher, Quoc Tran Dinh +2
Source separation or demixing is the process of extracting multiple components entangled within a signal. Contemporary signal processing presents a host of difficult source separat…
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…