Saturday Morning Videos: Workshop III: Geometry of Big Data, Geometry and Learning from Data in 3D and Beyond, IPAM

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So Workshop III: Geometry of Big Data, part of Geometry and Learning from Data in 3D and Beyond just finished at IPAM and we have videos ! Thanks to the organizers.

Pradeep Ravikumar (Carnegie Mellon University)

DAGs with NO TEARS: Continuous Optimization for Structure Learning 

Ronen Talmon (Technion - Israel Institute of Technology)

Data Analysis with the Riemannian Geometry of Symmetric Positive-Definite Matrices 

Yusu Wang (Ohio State University)

Metric learning for persistence-based summaries and application to graph classification

Dejan Slepcev (Carnegie Mellon University)

Proper regularizers for semi-supervised learning 

Jianfeng Lu (Duke University)

Solving for committor functions in high dimension

Lorenzo Rosasco (Università di Genova)

A consistent framework for structure machine learning 

Frederic Chazal (Institut National de Recherche en Informatique et Automatique (INRIA))

On the density of expected persistence diagrams and its kernel based estimation 

Richard Samworth (University of Cambridge)

Log-concave density estimation: adaptation and high dimensions 

Nathan Srebro (TTI-Chicago)

Infinite-Width Bounded-Norm Networks: A View from Function Space

Andrea Montanari (Stanford University)

Some geometric surprises in modern machine learning

Amit Singer (Princeton University)

Multi-target detection and cryo-EM imaging by autocorrelation analysis

Rebecca Willett (University of Chicago)

Learning to Solve Inverse Problems in Imaging 

Bin Dong (Peking University)

Bridging Deep Neural Networks and Differential Equations for Image Restoration and Beyond 

Marina Meila (University of Washington)

Manifold Learning for the Sciences 

Mahdi Soltanolkotabi (University of Southern California (USC))

Towards demystifying over-parameterization in deep learning

Stanley Osher (University of California, Los Angeles (UCLA))

PDE Approaches for Deep Learning 

Imre Risi Kondor (University of Chicago)

Covariant neural networks for learning physical systems 

Naftali Tishby (Hebrew University)

Topological transitions (and symmetries) of input representations in Deep Learning

Richard Tsai (University of Texas at Austin)

Machine learning approaches for a few optimization problems involving lines-of-sight

Bin Yu (University of California, Berkeley (UC Berkeley))

PCS workflow, interpretable machine learning, and DeepTune

Hongkai Zhao (University of California, Irvine (UCI))

Intrinsic complexity: from approximation of random vectors and random fields to solutions of PDEs

Zuowei Shen (National University of Singapore)

Deep Approximation via Deep Learning 

Joel Tropp (California Institute of Technology)

SketchySVD 

Jianfeng Cai (Hong Kong University of Science and Technology)

Solving Systems of Phaseless Equations: Riemannian Optimization and Global Geometry

Anastasia Dubrovina (Lyft)

Structured embedding spaces for shape completion and synthesis

Chao Gao (University of Chicago)

Robust Estimation and Generative Adversarial Nets 

Mauro Maggioni (Duke University)

Learning Interaction kernels in agent-based systems

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