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Reinforcement Learning
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FDS Seminar: Ilyas Fatkhullin (ETHZ), “Toward Dependable Reinforcement Learning: Hidden Structures and Safe Optimization”
Zoom Password: 123 Abstract: Modern reinforcement learning (RL) has enabled impressive progress in sequential decision-making, yet state-of-the-art performance often requires enormous engineering efforts and still lacks reliable, theory-backed explanations. This talk develops an optimization-centric perspective on RL aimed at reliability: algorithms that remain stable in high-dimensional, stochastic, and inherently non-convex settings. I will walk you…
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FDS x Applied Physics Colloquium: Grant Rotskoff (Stanford), “Efficient variational inference with generative models”
Abstract: Neural networks continue to surprise us with their remarkable capabilities for high-dimensional function approximation. Applications of machine learning now pervade essentially every scientific discipline, but predictive models to describe the optimization dynamics, inference properties, and flexibility of modern neural networks remain limited. In this talk, I will introduce several approaches to both analyzing and…