Previous Conferences & Workshops

Apr
24
2020

Joint IAS/Princeton/Montreal/Paris/Tel-Aviv Symplectic Geometry Zoominar

The Geography of Immersed Lagrangian Fillings of Legendrian Submanifolds
9:00am|https://princeton.zoom.us/j/745635914

Given a smooth knot K in the 3-sphere, a classic question in knot theory is: What surfaces in the 4-ball have boundary equal to K? One can also consider immersed surfaces and ask a “geography” question: What combinations of genus and double points...

Apr
23
2020

Theoretical Machine Learning Seminar

Deep Generative models and Inverse Problems
Alexandros Dimakis
3:00pm|https://theias.zoom.us/j/384099138

Modern deep generative models like GANs, VAEs and invertible flows are showing amazing results on modeling high-dimensional distributions, especially for images. We will show how they can be used to solve inverse problems by generalizing compressed...

Apr
23
2020

Joint IAS/Princeton University Number Theory Seminar

Symmetric power functoriality for holomorphic modular forms
Jack Thorne
9:00am|https://theias.zoom.us/j/959183254

Langlands’s functoriality conjectures predict the existence of “liftings” of automorphic representations along morphisms of L-groups. A basic case of interest comes from the irreducible algebraic representations of GL(2), thought of as the L-group...

Apr
22
2020

Mathematical Conversations

Sullivan's Clock: Dennis Sullivan's counter-example to the periodic orbit conjecture
5:30pm|Remote Access Only

In 1976 Dennis Sullivan gave an example of a smooth vector-field on a compact (Riemannian) 5-dimensional manifold in which all the orbits are closed but for which there is no upper bound to the length of a closed orbit. (At first this doesn't even...

Apr
21
2020

Theoretical Machine Learning Seminar

Assumption-free prediction intervals for black-box regression algorithms
Aaditya Ramdas
12:00pm|https://theias.zoom.us/j/384099138

There has been tremendous progress in designing accurate black-box prediction methods (boosting, random forests, bagging, neural nets, etc.) but for deployment in the real world, it is useful to quantify uncertainty beyond making point-predictions...

Apr
21
2020

Computer Science/Discrete Mathematics Seminar II

Non-commutative optimization: theory, algorithms and applications (or, can we prove P!=NP using gradient descent)
10:30am|https://theias.zoom.us/j/360043913

This talk aims to summarize a project I was involved in during the past 5 years, with the hope of explaining our most complete understanding so far, as well as challenges and open problems. The main messages of this project are summarized below; I...

Apr
20
2020

Analysis Seminar

A variational approach to the regularity theory for the Monge-Ampère equation
Felix Otto
11:00am|https://theias.zoom.us/j/562592856

We present a purely variational approach to the regularity theory for the Monge-Ampère equation, or rather optimal transportation, introduced with M. Goldman. Following De Giorgi’s philosophy for the regularity theory of minimal surfaces, it is...

Apr
20
2020

Computer Science/Discrete Mathematics Seminar I

Structure vs Randomness in Complexity Theory
Rahul Santhanam
11:00am|https://theias.zoom.us/j/360043913

The dichotomy between structure and randomness plays an important role in areas such as combinatorics and number theory. I will discuss a similar dichotomy in complexity theory, and illustrate it with three examples of my own work: (i) An...

Apr
17
2020

Joint IAS/Princeton/Montreal/Paris/Tel-Aviv Symplectic Geometry Zoominar

Equivariant quantum operations and relations between them
Nicholas Wilkins
9:15am|https://princeton.zoom.us/j/745635914

There is growing interest in looking at operations on quantum cohomology that take into account symmetries in the holomorphic spheres (such as the quantum Steenrod powers, using a Z/p-symmetry). In order to prove relations between them, one needs to...

Apr
16
2020

Workshop on New Directions in Optimization, Statistics and Machine Learning

Steps towards more human-like learning in machines
Josh Tenenbaum
4:30pm|Virtual

There are several broad insights we can draw from computational models of human cognition in order to build more human-like forms of machine learning. (1) The brain has a great deal of built-in structure, yet still tremendous need and potential for...