## Seminar and Invited Talks

### Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs

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.

[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

Seminar at Inria WIDE, 2018.

Invited talk at the 4th GDR RSD and ASF Winter School on Distributed Systems and Networks, 2019.

[slides]

### Who started this rumor? Quantifying the natural differential privacy guarantees of gossip protocols

Talk at the Workshop on Privacy-Aware Distributed Machine Learning , 2018.

[slides]

### Privacy-Preserving Algorithms for Decentralized Collaborative Machine Learning

Seminar at Inria Sequel, Inria Multispeech, CMLA, Naver Labs Europe, Alan Turing Institute, Thales Research & Technology and Miles Paris Dauphine, 2017-2019.

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.

[slides]

### A Massively Distributed Algorithm for Private Averaging with Malicious Adversaries

Seminar at CRIStAL Lille (DaTinG department day), Statistics Seminar of Paris 6/7 and LTCI Télécom Paris, 2017-2018.

Invited talk at the DALI 2017 Workshop on Fairness and Privacy in Machine Learning, 2017.

[slides]

### Decentralized Estimation and Optimization of Pairwise Functions

Seminar at SIGMA (Centrale Lille), 2017.

[slides]

### Decentralized Collaborative Learning of Personalized Models over Networks

Seminar at Inria Magnet and LPD Lab - EPFL, 2016-2017.

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.

[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 Labs Paris and Inria Magnet, 2015-2016.

[slides]

### The Frank-Wolfe Algorithm: Recent Results and Applications to High-Dimensional Similarity Learning and Distributed Optimization

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.

Invited talk at ICML 2015 workshop "Greed is Great", 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.