Seminar and Invited Talks
Privacy in Decentralized Machine Learning
Invited talk at the 3rd Workshop on Principles of Distributed Learning, 2024.
[slides]
Auditing Privacy in Machine Learning with Attacks and Zero-Knowledge Proofs
Invited talk at the Workshop on AI Auditing, 2024.
Invited talk at Privacy Alpine Seminar (Privaski), 2024.
Contributed talk at CNIL Privacy Research Day, 2024.
[slides]
Differentially Private Optimization with Coordinate Descent and Fixed-Point Iterations
Invited talk at the Learning and Optimization in Côte d'Azur Workshop, 2024.
Seminar at Multidisciplinary Optimization Seminar in Toulouse, 2024.
[slides]
On the Impact of Differential Privacy on Fairness in Machine Learning
Invited talk at the AI-UK Workshop on Privacy and Fairness in AI for Health, 2023.
[slides]
Better Privacy Guarantees for Decentralized Learning
Invited talk at Workshop FL-Day - Decentralized Federated Learning: Approaches and Challenges, Université Paris Saclay, 2023.
Invited talk at Google Workshop on Federated Learning and Analytics, virtual event, 2022.
Invited talk at CIRM Conference on Learning and Optimization, 2022.
Seminar at SIGMA (Univ. Lille), 2023.
[slides]
Deploying Federated Learning across French Hospitals -- Lessons Learned
Invited talk at the Workshop of DSVD Chair, 2023.
Invited talk at the Medical Federated Learning Program - Open House, 2022.
[slides]
Differentially Private Machine Learning
Keynote talk at Journées de Statistique (JDS), 2022.
Invited talk at STIC AmSud, 2022.
[slides]
Learning Fair Scoring Functions for Bipartite Ranking
Invited talk at the Workshop on Ethical AI, 2022.
Invited talk at the Fairness Day in Lille, 2022.
[slides]
Differentially Private Speaker Anonymization
Invited talk at Privacy Alpine Seminar (Privaski), 2022.
[slides]
Federated Multi-Task Learning under a Mixture of Distributions
Invited talk at Google Workshop on Federated Learning and Analytics, virtual event, 2021.
Contributed talk at NeurIPS@Paris, 2021.
Seminar at Machine Learning in Montpellier, 2022.
[slides]
Privacy-Preserving Federated Learning
Invited talk at the Private Math-Stat Workshop, virtual event, 2022.
Invited talk at the FocusAI Workshop, Institut Henri Poincaré, 2021.
Invited talk at the Federated Learning Workshop, Sorbonne Université, 2021.
Seminar at DeepMind Paris, AXA R&D, LIS Marseille, Inria LinkMedia, 2022.
[slides]
Introduction to Federated Learning
Keynote talk at Conférence Française d'Apprentissage (CAp), 2022.
Invited talk at the Health and Privacy-Preserving Machine Learning Workshop, 2021.
Invited talk at the India-France Knowledge Summit (KS3), 2021.
Invited talk at the Hi! Paris Summer School on AI & Data for Science, Business and Society, virtual event, 2021.
Invited talk at the French-German Summer School on Artificial Intelligence with Industry, virtual event, 2021.
Invited talk at Inria Scientific Days, virtual event, 2021.
Invited talk at the Federated Learning Winter School, virtual event, 2020.
Seminar at IRT SystemX, GDR IA, AFIA, Qwant, Idemia, Machine Learning in Montpellier, 2021-2024.
[slides] [longer version] [shorter version]
An Introduction to Differentially Private Data Analysis
Seminar at SAMM Paris, IMAG Montpellier, 2021.
[slides]
Privacy-Preserving Decentralized Machine Learning
Invited talk at the International Workshop and Privacy and Data Anonymization, virtual event, 2020.
Invited talk at the CNIL/Inria Workshop on AI & Privacy, virtual event, 2020.
Seminar at GT-PVP, LINC seminar at CNIL, Inria/Inserm HeKA, 2021.
[slides]
Efficient Differentially Private Averaging with Trusted Curator Utility and Robustness to Malicious Parties
Invited talk at Google Workshop on Federated Learning and Analytics, virtual event, 2020.
[slides]
Grand débat et IA : quelle transparence pour le traitement des données ? Une tentative de rétro-ingénierie de la synthèse
Contributed talk at Journées d'étude "Quels outils d'analyse pour les gilets jaunes ?", Sciences Po Paris, 2020.
[slides]
Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs
Invited talk at Google Workshop on Federated Learning and Analytics, 2019.
Invited talk at the workshop Graph signals: learning and optimization perspectives, 2019.
Invited talk at the workshop The power of graphs in machine learning and sequential decision making, 2019.
Invited talk at the Applied Machine Learning Days, 2020.
Invited talk at the CIRM Conference on Optimization for Machine Learning, 2020.
Invited talk at the International Workshop on Distributed Cloud Computing, 2020.
Seminar at Criteo AI Labs Paris, 2020.
[slides]
Artificial Intelligence & Privacy Protection
Invited talk at the kick-off seminar of
the HumAIn Alliance in Artificial Intelligence, 2019.
[slides]
Collaborative Machine Learning in Large-Scale Peer-to-Peer Distributed Systems
Invited talk at the 4th GDR RSD and ASF Winter School on Distributed Systems and Networks, 2019.
Seminar at Inria WIDE, 2018.
[slides]
Who started this rumor? Quantifying the natural differential privacy guarantees of gossip protocols
Contributed talk at the Workshop on Privacy-Aware Distributed Machine Learning , 2018.
[slides]
Privacy-Preserving Algorithms for Decentralized Collaborative Machine Learning
Invited talk at the 2nd Russian-French Workshop in Big Data and Applications, 2017.
Invited talk at the EPFL-Inria Workshop, 2018.
Invited talk at the Journées de Statistique (session SSFAM), 2018.
Invited talk at the CIFAR-UKRI-CNRS workshop on AI & Society: From principles to practice, 2019.
Invited talk at the International Workshop on Machine Learning & Artificial Intelligence, 2019.
Seminar at Inria Sequel, Inria Multispeech, CMLA, Naver Labs Europe, Alan Turing Institute, Thales Research & Technology and Miles Paris Dauphine, 2017-2019.
[slides]
A Massively Distributed Algorithm for Private Averaging with Malicious Adversaries
Invited talk at the DALI 2017 Workshop on Fairness and Privacy in Machine Learning, 2017.
Seminar at CRIStAL Lille (DaTinG department day), Statistics Seminar of Paris 6/7 and LTCI Télécom Paris, 2017-2018.
[slides]
Decentralized Estimation and Optimization of Pairwise Functions
Seminar at SIGMA (Univ. Lille), 2017.
[slides]
Decentralized Collaborative Learning of Personalized Models over Networks
Invited talk at the Workshop on Distributed Machine Learning (Télécom Paris), 2016.
Invited talk at the Journée Apprentissage et Interactions du GdR IA (UPMC), 2017.
Seminar at Inria Magnet and LPD Lab - EPFL, 2016-2017.
[slides]
U-Statistics in Machine Learning: Large-Scale Minimization and Decentralized Estimation
Seminar at EURA NOVA, Inria Magnet, LaHC Saint-Étienne and Proba/Stat Lille, 2016-2017.
[slides]
Metric Learning for Large-Scale Data
Seminar Statistical Machine Learning (SMILE) in Paris, 2016.
[slides]
Similarity and Distance Metric Learning with Applications to Computer Vision
Tutorial at ECML/PKDD 2015 (with Matthieu Cord), 2015.
[slides] [tutorial page]
Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics
Invited talk at the International Workshop on Machine learning, Optimization and Big Data (MOD 2015), 2015.
Large-Scale Similarity and Distance Metric Learning
Seminar at Criteo AI Labs Paris and Inria Magnet, 2015-2016.
[slides]
The Frank-Wolfe Algorithm: Recent Results and Applications to High-Dimensional Similarity Learning and Distributed Optimization
Invited talk at ICML 2015 workshop "Greed is Great", 2015.
Seminar at LIP6 UPMC, LaHC Saint-Étienne, Heudiasyc Compiègne, LIF Marseille, Inria Magnet, LIG Grenoble, CEREMADE Paris Dauphine, IMT Toulouse, LRI Inria/Paris Sud, AgroParisTech, Séminaire Parisien de Statistique and ENS/Inria Paris, 2014-2015.
[slides]
Metric Learning (and incidentally some distributed optimization)
Seminar at LTCI Télécom Paris, 2014.
[slides]
Tutorial on Metric Learning
Course / tutorial at the Université Catholique de Louvain for the CIL Doctoral School, 2013.
[slides]
Supervised Metric Learning with Generalization Guarantees
Seminar at Machine Learning Group Louvain, ENS/Inria Paris, LIRIS Lyon, LITIS Rouen, LIG Grenoble and USC Machine Learning, 2012-2013.