Previous Conferences & Workshops

Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, individual genomic assays measure elements that interact in vivo as...

Few-shot classification, the task of adapting a classifier to unseen classes given a small labeled dataset, is an important step on the path toward human-like machine learning. I will present some of the key advances in this area, and will then...

Organizers:

The workshop was organized by Sanjeev Arora (IAS/Princeton University), Roger Grosse (IAS/Univ Toronto), Sham Kakade (Univ Washington), Bin Yu (IAS/UC Berkeley).

We sought to bring together a diverse group of researchers, focusing...

Deep learning has led to rapid progress being made in the field of machine learning and artificial intelligence, leading to dramatically improved solutions of many challenging problems such as image understanding, speech recognition, and control...

Consider a (possibly time-dependent) vector field $v$ on the Euclidean space. The classical Cauchy-Lipschitz (also named Picard-Lindel\"of) Theorem states that, if the vector field $v$ is Lipschitz in space, for every initial datum $x$ there is a...

There are significant gaps between legal and technical thinking around data privacy. Technical standards such as k-anonymity and differential privacy are described using mathematical language whereas legal standards are not rigorous from a...

Compatible almost-complex structures on symplectic manifolds correspond to optimal quantizations. I will discuss this statement (joint with Louis Ioos and David Kazhdan), as well as some other geometric facets of uncertainty principles in quantum...

In the absence of a vaccine, isolation is about the only available means to control an epidemic. I would like to share with everyone some things I learned from a project I worked on a few years ago studying the consequences of delays and...

The Kudla-Rapoport conjecture predicts a precise identity between the arithmetic intersection number of special cycles on unitary Rapoport-Zink spaces and the derivative of local representation densities of hermitian forms. It is a key local...

Meta-learning (or learning to learn) studies how to use machine learning to design machine learning methods themselves. We consider an optimization-based formulation of meta-learning that learns to design an optimization algorithm automatically...