93 citations · 153 across the 5 of their papers we have counts for
6 papers
Diagonal Rescaling For Neural Networks
Jean Lafond, Nicolas Vasilache, Léon Bottou
We define a second-order neural network stochastic gradient training algorithm whose block-diagonal structure effectively amounts to normalizing the unit activations. Investigating…
Decentralized Frank-Wolfe Algorithm for Convex and Non-convex Problems
Hoi-To Wai, Jean Lafond, Anna Scaglione +1
Decentralized optimization algorithms have received much attention due to the recent advances in network information processing. However, conventional decentralized algorithms base…
On the Online Frank-Wolfe Algorithms for Convex and Non-convex Optimizations
Jean Lafond, Hoi-To Wai, Eric Moulines
In this paper, the online variants of the classical Frank-Wolfe algorithm are considered. We consider minimizing the regret with a stochastic cost. The online algorithms only requi…
Low Rank Matrix Completion with Exponential Family Noise
Jean Lafond
The matrix completion problem consists in reconstructing a matrix from a sample of entries, possibly observed with noise. A popular class of estimator, known as nuclear norm penali…
Probabilistic low-rank matrix completion on finite alphabets
Jean Lafond, Olga Klopp, Eric Moulines +1
The task of reconstructing a matrix given a sample of observedentries is known as the matrix completion problem. It arises ina wide range of problems, including recommender systems…
Adaptive Multinomial Matrix Completion
Olga Klopp, Jean Lafond, Eric Moulines +1
The task of estimating a matrix given a sample of observed entries is known as the \emph{matrix completion problem}. Most works on matrix completion have focused on recovering an u…