Tutorials

2024 “Scaling and Reliability Foundations in Machine Learning“, ISIT, Athens, Greece.

2023 “Deep Learning Theory for Computer Vision,” CVPR, Vancouver, CA.

2023 “Neural networks: the good, the bad, and the ugly,” ICASSP, Rhodes, Greece.

2023 “Polynomial nets in deep learning architectures,” AAAI, Washington DC, USA.

2022 “High-degree polynomial networks for image generation and recognition,” CVPR, New Orleans, USA.

2021 “Optimization Challenges in Adversarial Machine Learning,” Data Science Summer School, Paris, France.

2020 “Adaptive Optimization Methods for Machine Learning and Signal Processing,” EUSIPCO, Netherlands.

• Adaptive Optimization Methods for Machine Learning and Signal Processing (Part I/IV: An introduction)
• Adaptive Optimization Methods for Machine Learning and Signal Processing (Part II/IV: Introduction to adaptive first-order methods)
• Adaptive Optimization Methods for Machine Learning and Signal Processing (Part III/IV: Adaptive first-order methods)
• Adaptive Optimization Methods for Machine Learning and Signal Processing (Part IV/IV: Adaptivity in min-max optimization)

2019 “Mathematics of Data,” Data Science Summer School, Paris, France.

2018 “Mathematics of Data,” Department of Applied Mathematics and Theoretical Physics, Cambridge University, UK

WCS 2013

• Compressed Sensing: Motivation and geometric insights
• Compressed Sensing: Algorithms for low-dimensional models
• Compressed Sensing: Compressible priors
• Compressed Sensing: Nonparametric function learning

ICASSP 2015

• Convex Optimisation for Big Data (ICASSP 2015)
• Convex and non-convex approaches for low-dimensional models

IPSN

• Compressive sensing (Part 1)
• Compressive sensing (Part 2)

ICDCS

• Compressive sensing