These videos are freely available at https://www.youtube.com/c/instituteofmathematicalstatistics/videos. Here’s the list so far.

[IMS ML = IMS Medallion Lecture]

BS/IMS Schramm Lecture Balloons in Space(s) – Omer Angel

IMS Wald Lectures I: Random Walks and Fractal Graphs; II: Low Dimensional Random Fractals; and III: Higher Dimensional Spaces – Martin Barlow

IMS ML: Gambler’s Ruin Problems – Laurent Saloff-Coste

IMS ML: Random Determinants and the Elastic Manifold – Gérard Ben Arous

IMS ML: Simplicity and complexity of belief-propagation – Elchanan Mossel

BS/IMS Doob Lecture: Parking on Cayley trees and Frozen Erdős–Rényi – Nicolas Curien

IMS Brown Award: Toward instance-optimal reinforcement learning – Ashwin Pananjady

IMS Brown Award: Efficient manifold approximation with Spherelets – Didong Li

IMS Brown Award: Bayesian pyramids: Identifying interpretable discrete latent structures from discrete data – Yuqi Gu

IMS Blackwell Lecture: Estimating the mean of a random vector – Gabor Lugosi

IMS ML: Selective inference for trees – Daniela Witten

IMS ML: High-dimensional interpolators: From linear regression to neural tangent models – Andrea Montanari

IMS Wald Lectures I & II: Modeling and Estimating Large Sparse Networks I & Modeling and Estimating Large Sparse Networks II – Jennifer Chayes

IMS President Address: Proactive and All-Encompassing Statistics – Regina Liu

IMS ML: DNA Copy Number Profiling from Bulk Tissues to Single Cells – Nancy Zhang

IMS Lawrence D. Brown PhD Student Award Session 2021: First-Order Newton-Type Estimator for Distributed Estimation and Inference – Yichen Zhang; Minimax Optimality of Permutation Tests – Ilmun Kim; and Inference in Interpretable Latent Factor Regression Models – Xin Bing

IMS ML: What Kinds of Functions Do Neural Networks Learn? – Robert Nowak

IMS ML: Empirical Optimal Transport: Inference, Algorithms, Applications – Axel Munk

IMS Le Cam Lecture: Understanding Spectral Embedding – Jianqing Fan

IMS ML: Statistical Optimal Transport – Philippe Rigollet