Search Results for author: Igor Colin

Found 9 papers, 0 papers with code

Refined bounds for randomized experimental design

no code implementations22 Dec 2020 Geovani Rizk, Igor Colin, Albert Thomas, Moez Draief

Experimental design is an approach for selecting samples among a given set so as to obtain the best estimator for a given criterion.

Experimental Design

Best Arm Identification in Graphical Bilinear Bandits

no code implementations14 Dec 2020 Geovani Rizk, Albert Thomas, Igor Colin, Rida Laraki, Yann Chevaleyre

We study the best arm identification problem in which the learner wants to find the graph allocation maximizing the sum of the bilinear rewards.

A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration

no code implementations NeurIPS 2020 Kevin Scaman, Ludovic Dos Santos, Merwan Barlier, Igor Colin

This novel smoothing method is then used to improve first-order non-smooth optimization (both convex and non-convex) by allowing for a local exploration of the search space.

Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning

no code implementations NeurIPS 2019 Igor Colin, Ludovic Dos Santos, Kevin Scaman

For smooth convex and non-convex objective functions, we provide matching lower and upper complexity bounds and show that a naive pipeline parallelization of Nesterov's accelerated gradient descent is optimal.

Parallel Contextual Bandits in Wireless Handover Optimization

no code implementations21 Jan 2019 Igor Colin, Albert Thomas, Moez Draief

As cellular networks become denser, a scalable and dynamic tuning of wireless base station parameters can only be achieved through automated optimization.

Multi-Armed Bandits

Decentralized Topic Modelling with Latent Dirichlet Allocation

no code implementations5 Oct 2016 Igor Colin, Christophe Dupuy

Privacy preserving networks can be modelled as decentralized networks (e. g., sensors, connected objects, smartphones), where communication between nodes of the network is not controlled by an all-knowing, central node.

Topic Models

Extending Gossip Algorithms to Distributed Estimation of U-Statistics

no code implementations NeurIPS 2015 Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon

Efficient and robust algorithms for decentralized estimation in networks are essential to many distributed systems.

Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics

no code implementations12 Jan 2015 Stéphan Clémençon, Aurélien Bellet, Igor Colin

In a wide range of statistical learning problems such as ranking, clustering or metric learning among others, the risk is accurately estimated by $U$-statistics of degree $d\geq 1$, i. e. functionals of the training data with low variance that take the form of averages over $k$-tuples.

Metric Learning Model Selection

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