Institute for Pure & Applied Mathematics (IPAM) · Science

Institute for Pure & Applied Mathematics (IPAM)

The Institute for Pure and Applied Mathematics (IPAM) is a National Science Foundation Math Institute at the University of California, Los Angeles (UCLA) that fosters the interaction of mathematics with a broad range of science and technology, builds new interdisciplinary research communities, promotes mathematical innovation, and engages and transforms the world through mathematics. IPAM fulfills its mission through workshops and programs that connect mathematics and other disciplines or multiple areas of mathematics. These activities bring in thousands of visitors annually from academia, government, and industry. The NSF has encouraged IPAM to make the scientific presentations of our speakers available to a wider audience. While this YouTube channel contains many of our most popular videos, our full video archive is available at www.ipam.ucla.edu/videos.

Institute for Pure & Applied Mathematics (IPAM)

Episodes

Ellen Vitercik: "How much data is sufficient to learn high-performing algorithms?"

Wouter Kool: Deep Learning for Combinatorial Optimization: count your flops & make your flops count!

Maxime Gasse: "Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers"

Santanu Dey: "Solving SDPs by using sparse PCA"

Laurent Charlin: "Exact Combinatorial Optimization with Graph Convolutional Neural Networks"

Joan Bruna: "On Sparse Linear Programming and (simple) neural networks"

Vinod Nair: "Solving Mixed Integer Programs Using Neural Networks"

Petar Veličković: "Reasoning on Natural Inputs"

Le Song: "A Framework For Differentiable Discovery Of Graph Algorithms"

Emma Frejinger: "Can ML Help in Solving Cargo Capacity Management Booking Control Problems?"

Joseph Huchette: "Neural network verification as piecewise linear optimization"

Yunhao Tang: "Reinforcement Learning for Integer Programming: Learning to Cut"

Thiago Serra: "Scaling Up Exact Neural Network Compression by ReLU Stability"

Sebastian Pokutta: "Structured ML Training via Conditional Gradients"

Kyle Cranmer: "Quarks, hierarchical clustering, and combinatorial optimization"

Xavier Bresson: "The Transformer Network for the Traveling Salesman Problem"

Ron Kimmel: "Learning Geometry"

Oktay Günlük: "Fair and Interpretable Decision Rules for Binary Classification"

David Sutter: "A chain rule for the quantum relative entropy"

Tryphon Georgiou: "Schrödinger bridges: classical and quantum"