Séminaire Data de l’ENS

As the cost and throughput of genomic technologies reach a point where DNA sequencing is close to becoming a routine exam at the clinics, there is a lot of hope that treatments of diseases like cancer can dramatically improve by a digital revolution in medicine, where smart algorithms analyze « big medical data » to help doctors take the best decisions for each patient or to suggest new directions for drug development. While artificial intelligence and machine learning-based algorithms have indeed had a great impact on many data-rich fields, their […]

There is a deep analogy between statistical inference and statistical physics

Les problèmes de raisonnement inductif ou d'extrapolation comme 'deviner la suite d'une série de nombres', ou plus généralement, 'comprendre la structure cachée dans des observations', sont fondamentaux si l'on veut un jour construire une intelligence artificielle. On a parfois l'impression que ces problèmes ne sont pas mathématiquement bien définis. Or il existe une théorie mathématique rigoureuse du raisonnement inductif et de l'extrapolation, basée sur la théorie de l'information. Cette théorie est très élégante, mais difficile à appliquer. En pratique aujourd'hui, ce sont les réseaux de neurones qui donnent les meilleurs […]

The recent success of machine learning suggests that neural networks may be capable of approximating high-dimensional functions with controllably small errors. As a result, they could outperform standard function interpolation methods that have been the workhorses of scientific computing but do not scale well with dimension. In support of this prospect, here I will review what is known about the trainability and accuracy of shallow neural networks, which offer the simplest instance of nonlinear learning in functional spaces that are fundamentally different from classic approximation spaces. The dynamics of training […]

The young field of Machine learning has changed the ways we interact with data and neural networks have made us appreciate the potential of working with millions of parameters. Interestingly, the vast majority of scientific discoveries today are not based on these new techniques. I will discuss the contrast between these two regimes and I will show how an intermediate approach, i.e. neural network inspired but mathematically defined statistics (scattering and phase harmonic transforms), can provide the long-awaited tools in scientific research. I will illustrate these points using astrophysics as […]

Florence d'Alché-Buc (Telecom ParisTech) Title: Learning to predict complex outputs: a kernel view Abstract: Motivated by prediction tasks such as molecule identification or functional regression, we propose to leverage the notion of kernel to take into account the nature of output variables whether they be discrete structures or functions. This approach boils down to encode output data as vectors of the Reproducing kernel Hilbert Space associated to the so-called output kernel. We present vector-valued kernel machines to implement it and discuss different learning problems linked with the chosen loss function. Eventually large scale […]

Gerard Ben Arous (New York University) Title: Effective dynamics and critical scaling for Stochastic Gradient Descent in high dimensions Abstract: SGD in high dimension is a workhorse for high dimensional statistics and machine learning, but understanding its behavior in high dimensions is not yet a simple task. We study here the limiting 'effective' dynamics of some summary statistics for SGD in high dimensions, and find interesting and new regimes, i.e. not the expected one given by the population gradient flow. We find that a new corrector term is needed and that the phase […]

Chris Wiggins (Columbia & NYT) Data Science @ New York Times  The Data Science group at The New York Times develops and deploys machine learning solutions to newsroom and business problems. Re-framing real-world questions as machine learning tasks requires not only adapting and extending models and algorithms to new or special cases but also sufficient breadth to know the right method for the right challenge. I'll first outline how  - unsupervised,  - supervised, and  - reinforcement learning methods are increasingly used in human applications for  - description,  - prediction, and […]

Freddy Bouchet (ENS Lyon) Probabilistic forecast of extreme heat waves using convolutional neural networks and rare event simulations Understanding extreme events and their probability is key for the study of climate change impacts, risk assessment, adaptation, and the protection of living beings. Extreme heatwaves are, and likely will be in the future, among the deadliest weather events. Forecasting their occurrence probability a few days, weeks, or months in advance is a primary challenge for risk assessment and attribution, but also for fundamental studies about processes, dataset and model validation, and […]