IC
  • Bio
  • Papers
  • Experience
  • Teaching
  • Publications
    • Decentralized Ranking Aggregation via Gossip: Convergence and Robustness
    • On Gossip Algorithms for Machine Learning with Pairwise Objectives
    • Fast and Efficient Asynchronous Gossip Algorithm for Robust and Non-Smooth Convex Decentralized Learning
    • Robust Distributed Estimation: Extending Gossip Algorithms to Ranking and Trimmed Means
    • Adaptive Sample Sharing for Multi Agent Linear Bandits
    • Differentially Private Policy Gradient
    • Asynchronous Gossip Algorithms for Rank-Based Statistical Methods
    • 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
    • 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 α-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
    • Theoretical limits of pipeline parallel optimization and application to distributed deep learning
    • Parallel Contextual Bandits in Wireless Handover Optimization
    • 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
    • Extending gossip algorithms to distributed estimation of U-statistics
    • Learning information cascades in social networks
  • Experience
  • Teaching
    • Sequential Decision

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
← Refined bounds for randomized experimental design Jan 1, 2020
Parallel Contextual Bandits in Wireless Handover Optimization Jan 1, 2018 →

© 2026 Igor Colin. This work is licensed under CC BY NC ND 4.0

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