Previous Conferences & Workshops

I will explain the source of Grothendieck philosophy of motives, and tell of applications.

Causal inference from observational data is a vital problem, but it comes with strong assumptions. Most methods require that we observe all confounders, variables that affect both the causal variables and the outcome variables. But whether we have...

I will discuss some recent results showing that the sum-of-squares SDP hierarchy can be used to find approximately optimal solutions to k-CSPs, provided that the instance satisfies certain expansion properties. These properties can be shown to...

To solve the inverse spectral problem for the Schrödinger equation on a metric graph one needs to determine:
• the metric graph;
• the potential in the Schrödinger equation;
• the vertex conditions (connecting the edges together).
The inverse...

Function approximators, like deep neural networks, play a crucial role in building machine-learning based intelligent systems. This talk covers three core problems of function approximators: understanding function approximators, designing new...

I will talk about an approach to proving exponential mixing for some kinetic, non-diffusive stochastic processes, that have recently become popular in computational statistics community.

This talk presents evidence that humans learn complex functions by harnessing compositionality: complex structure is decomposed into simpler building blocks. I formalize this idea in the framework of Bayesian nonparametric regression using a grammar...

In the talk we will focus on certain constructions of weak solutions to the Navier--Stokes inequality (NSI), \[ u \cdot \left( u_t - \nu \Delta + (u\cdot \nabla ) u+ \nabla p \right) \leq 0\] on $\mathbb R^3$. Such vector fields satisfy both the...