Data Driven Learning and Control seminar series is organized by the Information and Decision Science Lab at Cornell University and aims to explore the latest advancements and interdisciplinary approaches to data-driven learning and control systems.
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Universal Learning in Nonlinear Dynamics & Neural Architectures
Can we build neural architectures that go beyond Transformers by leveraging principles from dynamical systems? In this talk, I will introduce a novel approach to sequence modeling that draws inspiration from the emerging paradigm of online control. The method is based on a new algorithm for learning in nonlinear dynamical systems, to achieve efficient long-range memory, fast inference, and provable robustness. I will present theoretical insights, empirical results. and recent advancements in fast sequence generation and provable length generalization.
Bio: Elad Hazan is a professor of computer science at Princeton University. His research focuses on the design and analysis of algorithms for basic problems in machine learning and optimization. Among his contributions are the co-invention of the AdaGrad algorithm for deep learning, the first sublinear-time algorithms for convex optimization, and online nonstochastic control theory. He is the recipient of the Bell Labs Prize, the IBM Goldberg best paper award twice, a European Research Council grant, a Marie Curie fellowship and twice the Google Research Award. He served on the steering committee of the Association for Computational Learning and was program chair for the Conference on Learning Theory 2015. He is the co-founder and director of Google AI Princeton.