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Institute for Pure & Applied Mathematics (IPAM) ยท 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.

Episodes

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Qi Gong: "Nonlinear optimal feedback control - a model-based learning approach"

30 Jun 2020
57m
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Levon Nurbekyan: "Computational methods for mean-field games (Part 2/2)"

29 Jun 2020
1h 16m
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Levon Nurbekyan: "Computational methods for mean-field games (Part 1/2)"

29 Jun 2020
1h 17m
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Jeff Calder: "An intro to concentration of measure with applications to graph-based l... (Part 2/2)"

29 Jun 2020
1h 13m
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Jeff Calder: "An intro to concentration of measure with applications to graph-based l... (Part 1/2)"

29 Jun 2020
1h 12m
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Wilfrid Gangbo: "Analytical Aspect of Mean Field Games (Part 2/2)"

29 Jun 2020
1h 21m
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Wilfrid Gangbo: "Analytical Aspect of Mean Field Games (Part 1/2)"

29 Jun 2020
1h 4m
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Claire Tomlin: "Towards Real-Time Reachability (Part 2/2)"

29 Jun 2020
1h 16m
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Claire Tomlin: "Towards Real-Time Reachability (Part 1/2)"

29 Jun 2020
1h 15m
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Adam Oberman: "Gradient descent, Stochastic gradient descent, and acceleration" (Part 2/2)

29 Jun 2020
1h 2m
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Adam Oberman: "Generalization Theory in Machine Learning" (Part 1/2)

29 Jun 2020
1h 11m
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Angela Schoellig: "Machine Learning for Robotics: Achieving Safety, Performance and Reliability..."

09 Apr 2020
49m
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Lieven Vandenberghe: "Bregman proximal methods for semidefinite optimization."

09 Apr 2020
48m
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Stephen Wright: "Nonconvex optimization in matrix optimization and distributionally robust optim..."

09 Apr 2020
54m
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Dorsa Sadigh: "Beyond Theory of Mind: Learning and Influencing Conventions in Multi-Agent Intera..."

09 Apr 2020
55m
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Richard Murray: "Can We Really Use Machine Learning in Safety Critical Systems?"

09 Apr 2020
49m
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Francesco Borrelli: "Sample-Based Learning Model Predictive Control"

09 Apr 2020
47m
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Melanie Zeilinger: "Learning-based Model Predictive Control - Towards Safe Learning in Control"

09 Apr 2020
51m
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Zico Kolter: "Integrating optimization, constraints, and control within deep learning models"

09 Apr 2020
1h 0m
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Daniel Kuhn: "Wasserstein Distributionally Robust Optimization: Theory and Applications in Machi..."

09 Apr 2020
1h 1m
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