29 citations · 104 across the 16 of their papers we have counts for
7 papers · 1 filter
Matrix-wise -constrained Sparse Nonnegative Least Squares
Nicolas Nadisic, Jeremy E Cohen, Arnaud Vandaele +1
Nonnegative least squares problems with multiple right-hand sides (MNNLS) arise in models that rely on additive linear combinations. In particular, they are at the core of most non…
Robot Design With Neural Networks, MILP Solvers and Active Learning
Sanjai Narain, Emily Mak, Dana Chee +6
Central to the design of many robot systems and their controllers is solving a constrained blackbox optimization problem. This paper presents CNMA, a new method of solving this pro…
Continuous dictionaries meet low-rank tensor approximations
Clement Elvira, Jeremy E. Cohen, Cedric Herzet +1
In this short paper we bridge two seemingly unrelated sparse approximation topics: continuous sparse coding and low-rank approximations. We show that for a specific choice of conti…
A Flexible Optimization Framework for Regularized Matrix-Tensor Factorizations with Linear Couplings
Carla Schenker, Jeremy E. Cohen, Evrim Acar
Coupled matrix and tensor factorizations (CMTF) are frequently used to jointly analyze data from multiple sources, also called data fusion. However, different characteristics of da…
Sparse Separable Nonnegative Matrix Factorization
Nicolas Nadisic, Arnaud Vandaele, Jeremy E. Cohen +1
We propose a new variant of nonnegative matrix factorization (NMF), combining separability and sparsity assumptions. Separability requires that the columns of the first NMF factor…
Computing the proximal operator of the induced matrix norm
Jeremy E. Cohen
In this short article, for any matrix the proximity operator of two induced norms and are derived. Although no close form…
Accelerating Block Coordinate Descent for Nonnegative Tensor Factorization
Andersen Man Shun Ang, Jeremy E. Cohen, Nicolas Gillis +1
This paper is concerned with improving the empirical convergence speed of block-coordinate descent algorithms for approximate nonnegative tensor factorization (NTF). We propose an…