Previous Conferences & Workshops

Feb
12
2018

Members’ Seminar

Cocycles, Lyapunov exponents, localization
2:00pm|Simonyi Hall 101

This talk will be an introduction to the methods used in the study of spectral properties of Schroedinger operators with a potential defined via the action of an ergodic transformation. Open problems relating to Lyapunov exponents over a skew shift...

Feb
12
2018

Computer Science/Discrete Mathematics Seminar I

Nonlinear dimensionality reduction for faster kernel methods in machine learning.
Christopher Musco
11:00am|Simonyi Hall 101

The Random Fourier Features (RFF) method (Rahimi, Recht, NIPS 2007) is one of the most practically successful techniques for accelerating computationally expensive nonlinear kernel learning methods. By quickly computing a low-rank approximation for...

Feb
08
2018

Joint IAS/Princeton University Number Theory Seminar

The Galois action on the stable homology of symplectic groups over Z.
4:30pm|Fine 214, Princeton University

The Galois group of Q acts on the homology of the complex moduli space of abelian varieties, or, equivalently, on the homology of symplectic groups Sp_{2g}(Z). (Here we take homology with finite or profinite coefficients.) In particular, the Galois...

Feb
07
2018

Mathematical Conversations

An Introduction to Univalent Foundations
Daniel Grayson
6:00pm|Dilworth Room

The Univalent Foundations of Voevodsky offer not only a formal language for use in computer verification of proofs, but also a foundation of mathematics alternative to set theory, in which propositions and their proofs are mathematical objects, and...

Feb
07
2018

Analysis Seminar

Nodal sets of Laplace eigenfunctions
1:30pm|Simonyi Hall 101

Zero sets of Laplace eigenfunctions are called nodal sets. The talk will focus on propagation of smallness techniques, which are useful for estimates of the Hausdorff measure of the nodal sets.

Feb
06
2018

Computer Science/Discrete Mathematics Seminar II

Outlier-Robust Estimation via Sum-of-Squares
10:30am|Simonyi Hall 101

We develop efficient algorithms for estimating low-degree moments of unknown distributions in the presence of adversarial outliers. The guarantees of our algorithms improve in many cases significantly over the best previous ones, obtained in recent...