Statistics and Computer Science for Data Science at Paris-Saclay 2021

Explore cutting-edge developments in statistics and machine learning through this comprehensive conference featuring six expert presentations from leading researchers at Paris-Saclay. Delve into supervised learning techniques for handling missing data with Gaël Varoquaux, examining practical approaches to incomplete datasets. Analyze the mathematical foundations of wide neural networks through Lenaïc Chizat's gradient descent analysis on two-layer architectures. Discover advanced image restoration methods with Emilie Chouzenoux's deep unfolding approach to proximal interior point algorithms. Learn about maximum entropy distributions and their applications in image synthesis under statistical constraints from Agnès Desolneux. Gain insights into neural network perspectives on input similarity through Guillaume Charpiat's research. Explore the intersection of deep learning and audio processing with Gaël Richard's work on neural networks for audio and music transformations. This conference brings together mathematicians and computer scientists to present recent advances across machine learning, optimization, deep learning, and optimal transport, offering a comprehensive overview of current research trends in data science.