Slides

SNAPP, 2020

ICTP-SISSA, 2020

Open Online Probability Summer School, Lect 1, Lect 2, Lect 3, Lect 4, Lect 5, (references), 2020

ICM, Rio De Janeiro, 2018

IMS, Vilnius, 2018

COLT Plenary, Amsterdam, 2017

mathstatsxdonoho60, Stanford, 2017

CMU Machine Learning/Google Distinguished Lecture (+joint Stanford-Berkeley Colloquium), 2016

Allerton Tutorial, UIUC, 2015

NecSys Plenary, Santa Barbara, 2012

ITW Plenary, EPFL, 2012

Video

Self-induced regularization from linear regression to neural networks, Stochastic Networks, Applied Probability, and Performance, 2020

Mean field methods in high-dimensional statistics and non-convex optimization 1, 2, 3, 4, 5, Open Online Probability Summer School (OOPS), 2020

What is Machine Learning, And What We Don't Understand About It, ICTP-SISSA Colloquium, 2020

Mean Field Descriptions of Two Layers Neural Network, MIFODS - Workshop on Non-convex optimization and deep learning, MIT, 2019

Optimization of the Sherrington-Kirkpatrick Hamiltonian, MIT LIDS Students’ Conference, 2019

Algorithms and computational phase transitions in low-rank estimation, Institut des Hautes Etudes Scientifiques, Paris, 2018

Mean field asymptotics in high-dimensional statistics, ICM, Rio de Janeiro, 2018

Matrix and graph estimation, Institut Henri Poincare, Paris, 2017 (Part 2, Part 3, Part 4)

Phase transitions in semi-definite relaxations, CMU 2016

Analysis of Algorithms on Dense Matrices using Approximate Message Passing, Simons Institute, Berkeley, 2015

A Statistical Model for Tensor Principal Component Analysis, Simons Institute, Berkeley, 2014

Finding Small Structures in Large Datasets, Simons Institute, Berkeley, 2013

Universality in Compressed Sensing, BIRS, Canada, 2013

Lectures on Inference and Learning in Ising Models, Indian Institute of Science, Bangalore, 2012

The Set of Solutions of Random XORSAT Formulae, Stanford, 2011

Large Matrices beyond Singular Value Decomposition, ACM-LinkedIn, 2010

Newton Institute, Cambridge UK, 2010

STOC Tutorial, Cambridge, 2010