Hadrien Hendrikx

According to our database1, Hadrien Hendrikx authored at least 24 papers between 2016 and 2024.

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Bibliography

2024
Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits.
Trans. Mach. Learn. Res., 2024

Byzantine-Robust Gossip: Insights from a Dual Approach.
CoRR, 2024

The Relative Gaussian Mechanism and its Application to Private Gradient Descent.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Beyond Spectral Gap: The Role of the Topology in Decentralized Learning.
J. Mach. Learn. Res., 2023

Beyond spectral gap (extended): The role of the topology in decentralized learning.
CoRR, 2023

Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees.
Proceedings of the International Conference on Machine Learning, 2023

A principled framework for the design and analysis of token algorithms.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2021
Accelerated Methods for Distributed Optimization. (Méthodes Accélérées pour l'Optimisation Distribuée).
PhD thesis, 2021

An Optimal Algorithm for Decentralized Finite-Sum Optimization.
SIAM J. Optim., 2021

A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip.
CoRR, 2021

Decentralized Optimization with Heterogeneous Delays: a Continuous-Time Approach.
CoRR, 2021

Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Asynchrony and Acceleration in Gossip Algorithms.
CoRR, 2020

Who Started This Rumor? Quantifying the Natural Differential Privacy of Gossip Protocols.
Proceedings of the 34th International Symposium on Distributed Computing, 2020

Dual-Free Stochastic Decentralized Optimization with Variance Reduction.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Who started this rumor? Quantifying the natural differential privacy guarantees of gossip protocols.
CoRR, 2019

Asynchronous Accelerated Proximal Stochastic Gradient for Strongly Convex Distributed Finite Sums.
CoRR, 2019

An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex Objectives.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2017
Can AIs learn to avoid human interruption?
CoRR, 2017

Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016
Toward a route detection method base on detail call records.
Proceedings of the IEEE Latin American Conference on Computational Intelligence, 2016


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