IC
  • Bio
  • Papers
  • Experience
  • Teaching
  • Recent & Upcoming Talks
    • Example Talk
  • Publications
    • Wireless Parameter Tuning with Clustering Multi-Agents in Linear Stochastic Bandit
    • Differentially Private Model-Based Offline Reinforcement Learning
    • Measures of diversity and space-filling designs for categorical data
    • Method and system for a controller
    • Clustered Multi-Agent Linear Bandits
    • Multi-agent best arm identification with private communications
    • Price of Safety in Linear Best Arm Identification
    • Stable bounds on the duality gap of separable nonconvex optimization problems
    • An $$backslash$alpha $-No-Regret Algorithm For Graphical Bilinear Bandits
    • An $$backslash$alpha $-No-Regret Algorithm For Graphical Bilinear Bandits
    • Deciphering Lasso-based Classification Through a Large Dimensional Analysis of the Iterative Soft-Thresholding Algorithm
    • Best Arm Identification in Graphical Bilinear Bandits
    • A simple and efficient smoothing method for faster optimization and local exploration
    • Refined bounds for randomized experimental design
    • An example preprint / working paper
    • Theoretical limits of pipeline parallel optimization and application to distributed deep learning
    • Parallel Contextual Bandits in Wireless Handover Optimization
    • An approximate shapley-folkman theorem
    • An Approximate Shapley-Folkman Theorem
    • Adaptation des méthodes d’apprentissage aux U-statistiques
    • Adapting machine learning methods to U-statistics
    • Decentralized Topic Modelling with Latent Dirichlet Allocation
    • Gossip dual averaging for decentralized optimization of pairwise functions
    • Scaling-up empirical risk minimization: optimization of incomplete $ U $-statistics
    • Un algorithme de Gossip pour l’optimisation décentralisée de fonctions sur les paires
    • An example journal article
    • Extending gossip algorithms to distributed estimation of U-statistics
    • Learning information cascades in social networks
  • Projects
  • Blog
    • 🎉 Easily create your own simple yet highly customizable blog
    • 🧠 Sharpen your thinking with a second brain
    • 📈 Communicate your results effectively with the best data visualizations
    • 👩🏼‍🏫 Teach academic courses
    • ✅ Manage your projects
  • Projects
    • Pandas
    • PyTorch
    • scikit-learn
  • Experience
  • Teaching

Theoretical limits of pipeline parallel optimization and application to distributed deep learning

Jan 1, 2019·
Igor Colin
,
Ludovic Dos Santos
,
Kevin Scaman
· 0 min read
Cite
Type
Journal article
Publication
Advances in Neural Information Processing Systems
Last updated on Jan 1, 2019
← An example preprint / working paper Apr 7, 2019
Parallel Contextual Bandits in Wireless Handover Optimization Jan 1, 2018 →

© 2025 Me. This work is licensed under CC BY NC ND 4.0

Published with Hugo Blox Builder — the free, open source website builder that empowers creators.