The challenges of model-based reinforcement learning and how to overcome them

Csaba Szepesvári
University of Alberta
June 18, 2020
Some believe that truly effective and efficient reinforcement learning algorithms must explicitly construct and explicitly reason with models that capture the causal structure of the world. In short, model-based reinforcement learning is not optional. As this is not a new belief, it may be surprising that empirically, at least as far as the current state of art is concerned, the majority of the top performing algorithms are model-free.

Generalizable Adversarial Robustness to Unforeseen Attacks

Soheil Feizi
University of Maryland
June 23, 2020
In the last couple of years, a lot of progress has been made to enhance robustness of models against adversarial attacks. However, two major shortcomings still remain: (i) practical defenses are often vulnerable against strong “adaptive” attack algorithms, and (ii) current defenses have poor generalization to “unforeseen” attack threat models (the ones not used in training).

Instance-Hiding Schemes for Private Distributed Learning

Sanjeev Arora
Princeton University; Distinguishing Visiting Professor, School of Mathematics
June 25, 2020
An important problem today is how to allow multiple distributed entities to train a shared neural network on their private data while protecting data privacy. Federated learning is a standard framework for distributed deep learning Federated Learning, and one would like to assure full privacy in that framework . The proposed methods, such as homomorphic encryption and differential privacy, come with drawbacks such as large computational overhead or large drop in accuracy.

Distinguishing monotone Lagrangians via holomorphic annuli

Ailsa Keating
University of Cambridge
June 26, 2020
We present techniques for constructing families of compact, monotone (including exact) Lagrangians in certain affine varieties, starting with Brieskorn-Pham hypersurfaces. We will focus on dimensions 2 and 3. In particular, we'll explain how to set up well-defined counts of holomorphic annuli for a range of these families. Time allowing, we will give a number of applications.

Infinite staircases and reflexive polygons

Ana Rita Pires
University of Edinburgh
July 3, 2020
A classic result, due to McDuff and Schlenk, asserts that the function that encodes when a four-dimensional symplectic ellipsoid can be embedded into a four-dimensional ball has a remarkable structure: the function has infinitely many corners, determined by the odd-index Fibonacci numbers, that fit together to form an infinite staircase. The work of McDuff and Schlenk has recently led to considerable interest in understanding when the ellipsoid embedding function for other symplectic 4-manifolds is partly described by an infinite staircase.