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

Andreas Krause: "Safe and Efficient Exploration in Reinforcement Learning"

Anca Dragan: "Learning Intended Cost Functions: Extracting all the right information from all th..."

Emma Brunskill: "Curbing Our Enthusiasm: Constraining Decision Policies Learned from the Past to..."

Ben Recht: "Trying to Make Sense of Control from Pixels"

Martin Riedmiller: "Learning Control from Minimal Prior Knowledge"

Russell Tedrake: "From pixels to torques: output feedback for robotics"

Mengdi Wang: "On the statistical complexity of reinforcement learning"

Necmiye Ozay: "A fresh look at some classical system identification methods"

Csaba Szepesvari: "Model misspecification in reinforcement learning"

Dimitri Bertsekas: "Distributed and Multiagent Reinforcement Learning"

Moritz Diehl: "Convexity Exploiting Newton-Type Optimization for Learning and Control"

John Murray: "Neural Circuit Modeling of Large-Scale Brain Dynamics for Computational Psychiatry"

Michael Bonsall: "Multiscale mathematical approaches for translational mental health benefits"

Martin Paulus: "Decision-Making and Computational Psychiatry: An Explanatory and Pragmatic Persp..."

Deanna Barch: "Computational Contributions to Understanding Impaired Motivation and Effort in Ps..."

A. David Redish: "Cross-species translation through computational analyses - implications for un..."

Danilo Bzdok: "Algorithmic Analytics towards Precision Psychiatry"

Sarah Yip: "Connectome-based modeling of real world clinical outcomes in addictions"

Justin Baker: "Sensing Psychosis: Toward Robust Computational Phenotypes in Severe Mental Illness"

Shashaank Vattikuti: "Perceptual rivalry as a window into cognition"