Hadrien Hendrikx
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Briefly
I am a researcher (Chargé de Recherche) in the Thoth team at Inria Grenoble, a French public research institute. From October 2021 to December 2022, I was a post-doc in the MLO team from EPFL, working with Martin Jaggi. Before that (2018-2021), I was a Ph.D. student in the SIERRA and DYOGENE (now ARGO) teams from Inria Paris, which are also part of the Computer Science Department of Ecole Normale Supérieure. I was also part of the MSR-INRIA joint centre. I worked under the supervision of Francis Bach and Laurent Massoulié on decentralized optimization.
Prior to that, I graduated from Ecole Polytechnique in 2016 and got a master degree from EPFL in Computer Science (Master en Informatique) in 2018. During my master, I had the chance to work as a Research Assistant in the DCL lab under the supervision of Rachid Guerraoui and in close collaboration with Aurélien Bellet.
Contact
Physical address: Inria, 655 Av. de l'Europe, 38330 Montbonnot-Saint-Martin
E-mail: hadrien [dot] hendrikx [at] inria [dot] fr
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Openings
Please reach out by email if you're interested in collaboration!
Research interests
I am broadly interested in optimization for machine learning, regardless of the flavor: stochastic, accelerated, non-euclidean… My two main research directions in this area are distributed optimization (and in particular challenges tied to robustness, privacy and personalization), and mirror descent methods (and in particular how to develop a consistent stochastic theory).
I currently focus most of my energy on developing projects at the interface between machine learning and other scientific disciplines, and in particular ecology. I am for instance part of the CASCA project with the Eco&Sol lab, where we leverage modern computer vision to better understand underground fauna.
Please contact me for discussions and potential collaborations!
Teaching
2024 - 2025: Teacher, Numerical Optimization, M1 Applied Mathematics, Université Grenoble Alpes
2024 - 2025: Teacher (part-time), Generalizations Properties of Machine Learning Algorithms, M2 Mathématiques de l'aléatoire, Orsay.
2023 - 2024: Teacher, Numerical Optimization, M1 Applied Mathematics, Université Grenoble Alpes
2023 - 2024: Teacher (part-time), Generalizations Properties of Machine Learning Algorithms, M2 Mathématiques de l'aléatoire, Orsay.
2019 - 2020: Teaching assistant, Advanced Algorithms (L3 Informatique), University Paris Descartes
2018 - 2019: Teaching assistant, Advanced Algorithms (L3 Informatique), Logic (L1 Informatique) University Paris Descartes
Reviewing
Conferences: ICML 2019 (Top 5%), NeurIPS 2019 (Top 400), ICML 2020 (Top 33%), NeurIPS 2020 (Top 10%), AISTATS 2021, ICML 2021, NeurIPS 2021, AISTATS 2022, ICML 2022 (Top 10%), NeurIPS 2022, AISTATS 2023 (Top 10%), ICML 2023, AISTATS 2023, AISTATS 2024, ICML 2024, NeurIPS 2024.
Journals: Mathematical Programming, IEEE Transactions on Signal Processing, Automatica, SIOPT, JMLR
Supervision
Current:
Former:
Mohamed Bacar Abdoulandhum: Master Student, co-supervised with Léa Lugassy (PADV)
Daniel Morales Broton: full-time Master intern.
Abdellah El Mrini: Master Student at Ecole Polytechnique (Paris)
Rustem Islamov: Master Student at Ecole Polytechnique (Paris)
Mathieu Even: PhD student at INRIA Paris with Laurent Massoulié
Publications and preprints
H. Hendrikx, Investigating Variance Definitions for Stochastic Mirror Descent with Relative Smoothness. [arXiv:2404.12213], arXiv preprint, 2024.
R. Gaucher, A. Dieuleveut, H. Hendrikx, Byzantine-Robust Gossip: Insights from a Dual Approach. [arXiv:2405.03449], arXiv preprint, 2024.
D. Morales-Brotons, T. Vogels, H. Hendrikx, Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits, [arXiv:], Transactions on Machine Learning Research (TMLR), 2024.
H. Hendrikx, P. Mangold, A. Bellet. The Relative Gaussian Mechanism and its Application to Private Gradient Descent. [arXiv:2308.15250], International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.
A. Koloskova*, H. Hendrikx*, S. Stich. Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees. [arXiv:2305.01588], International Conference on Machine Learning (ICML), 2023.
T. Vogels*, H. Hendrikx*, M. Jaggi. Beyond spectral gap (extended): the role of the topology in decentralized learning. [arXiv:2301.02151], Journal of Machine Learning Research (JMLR), 2023.
H. Hendrikx. A principled framework for the design and analysis of token algorithms. [arXiv:2205.15015, video], International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.
T. Vogels*, H. Hendrikx*, M. Jaggi. Beyond spectral gap: the role of the topology in decentralized learning. [arXiv:2206.03093, video], Advances in Neural Information Processing Systems (NeurIPS), 2022.
H. Hendrikx. Accelerated Methods for Distributed Optimization. [HAL link], PhD Thesis, PSL Research university, 2021.
M. Even, H. Hendrikx, L. Massoulié. Decentralized Optimization with Heterogeneous Delays: a Continuous-Time Approach. [arXiv:2106.03585], arXiv preprint, 2021.
M. Even, R. Berthier, F. Bach, N. Flammarion, P. Gaillard, H. Hendrikx, L. Massoulié, A. Taylor A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip. [arXiv:2106.07644, video], Advances in Neural Information Processing Systems (NeurIPS), 2021. Oral Presentation. Outstanding paper award.
H. Hendrikx, F. Bach, L. Massoulié. An Optimal Algorithm for Decentralized Finite Sum Optimization. [arXiv:2005.10675], SIAM Journal on Optimization, 2021.
R. Dragomir*, M. Even*, H. Hendrikx*. Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction. [arXiv:2104.09813], International Conference on Machine Learning (ICML), 2021.
H. Hendrikx, F. Bach, L. Massoulié. Dual-Free Stochastic Decentralized Optimization with Variance Reduction. [arXiv:2006.14384], Advances in Neural Information Processing Systems (NeurIPS), 2020.
H. Hendrikx, L. Xiao, S. Bubeck, F. Bach, L. Massoulié. Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization. [arXiv:2002.10726, video], International Conference on Machine Learning (ICML), 2020.
A. Bellet, R. Guerraoui, H. Hendrikx. Who started this rumor? Quantifying the natural differential privacy guarantees of gossip protocols. [arXiv:1902.07138], International Symposium on DIStributed Computing (DISC), 2020.
H. Hendrikx, F. Bach, L. Massoulié. An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums. [arXiv:1905.11394], Advances in Neural Information Processing Systems (NeurIPS), 2019.
H. Hendrikx, F. Bach, L. Massoulié. Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex Objectives. [arXiv:1810.02660], International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
EME Mhamdi, R Guerraoui, H Hendrikx, A Maurer. Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning. [arXiv:1704.02882], Advances in Neural Information Processing Systems (NIPS), 2017. Spotlight presentation.
H. Hendrikx, M. Nuñez del Prado Cortez. Towards a route detection method based on detail call records. [IEEE Xplore 7885725], In Computational Intelligence (LA-CCI), 2016 IEEE Latin American Conference on (pp. 1-6). IEEE., 2016.
* denotes equal contribution
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