22 citations · 46 across the 7 of their papers we have counts for
15 papers
Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation (Extended Abstract)
Aleksandra Burashnikova, Yury Maximov, Marianne Clausel +3
This paper is an extended version of [Burashnikova et al., 2021, arXiv: 2012.06910], where we proposed a theoretically supported sequential strategy for training a large-scale Reco…
The Last-Iterate Convergence Rate of Optimistic Mirror Descent in Stochastic Variational Inequalities
Waïss Azizian, Franck Iutzeler, Jérôme Malick +1
In this paper, we analyze the local convergence rate of optimistic mirror descent methods in stochastic variational inequalities, a class of optimization problems with important ap…
Optimization in Open Networks via Dual Averaging
Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick +1
In networks of autonomous agents (e.g., fleets of vehicles, scattered sensors), the problem of minimizing the sum of the agents' local functions has received a lot of interest. We…
Nonsmoothness in Machine Learning: specific structure, proximal identification, and applications
Franck Iutzeler, Jérôme Malick
Nonsmoothness is often a curse for optimization; but it is sometimes a blessing, in particular for applications in machine learning. In this paper, we present the specific structur…
Randomized Progressive Hedging methods for Multi-stage Stochastic Programming
Gilles Bareilles, Yassine Laguel, Dmitry Grishchenko +2
Progressive Hedging is a popular decomposition algorithm for solving multi-stage stochastic optimization problems. A computational bottleneck of this algorithm is that all scenario…
Rank-one partitioning: formalization, illustrative examples, and a new cluster enhancing strategy
Charlotte Laclau, Franck Iutzeler, Ievgen Redko
In this paper, we introduce and formalize a rank-one partitioning learning paradigm that unifies partitioning methods that proceed by summarizing a data set using a single vector t…