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We would like you to present one of the articles listed below in groups of 1 to 3 students.
The list contains various articles around the topics of the course. Some are more theoretical, some are more algorithmic, others deal with applications. You can present an overview of the article or put an emphasis on a specific aspect that you find interesting.
- Presentation time: 10 min / team (including 4 min of questions).
- Date: January 13, 2019.
- The slides (in pdf) will be projected from our machine (if you want to present an implementation or a script run, you should prepare slides on it). The slides should be sent at least a day before the defense to cdo.grenoble@gmail.com.
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
Aaron Defazio, Francis Bach, Simon Lacoste-Julien
NeurIPS, 2014
ASAGA: Asynchronous Parallel SAGA
Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien
ICML, 2016
Prox-ASAGA: Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization
Fabian Pedregosa, Remi Leblond, Simon Lacoste-Julien
NeurIPS, 2016
SVRG: Accelerating Stochastic Gradient Descent using Predictive Variance Reduction
Rie Johnson, Tong Zhang
NeurIPS, 2013
Optimization Methods for Large-Scale Machine Learning
Léon Bottou, Frank E. Curtis, Jorge Nocedal
SIAM, 2018
A Distributed Flexible Delay-tolerant Proximal Gradient Algorithm
Konstantin Mishchenko, Franck Iutzeler, Jérôme Malick
ICML, 2018
CoCoA: A General Framework for Communication-Efficient Distributed Optimization
Virginia Smith, Simone Forte, Chenxin Ma, Martin Takac, Michael I. Jordan, Martin Jaggi
JMLR, 2018
ARock: an Algorithmic Framework for Asynchronous Parallel Coordinate Updates
Zhimin Peng, Yangyang Xu, Ming Yan, Wotao Yin
SIAM, 2016
Asynchronous stochastic convex optimization
John C. Duchi, Sorathan Chaturapruek, Christopher Ré
NeurIPS, 2015
Sparsified SGD with Memory
Sebastian U. Stich, Jean-Baptiste Cordonnier, Martin Jaggi
NeurIPS, 2018
A descent Lemma beyond Lipschitz gradient continuity: first-order methods revisited and applications
Heinz H. Bauschke, Jérôme Bolte, Marc Teboulle
Math of OR, 2016
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates
Sharan Vaswani, Aaron Mishkin, Issam Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien
NeurIPS, 2019
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent
Eduard Gorbunov MIPT, Filip Hanzely, Peter Richtárik
Preprint, 2019
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