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SUMMARY:ESE Spring Colloquium - "Provably Robust Algorithms for Prediction and Control"
DESCRIPTION:Feedback-driven decision-making systems are at the emerging frontier of machine learning applications. Upcoming applications of societal consequence\, such as self-driving vehicles and smartwatch-based health interventions\, have to contend with the challenge of operating in reactive stateful environments. In this talk\, I will describe my work on designing principled robust algorithms for feedback-driven learning\, with provable guarantees on computational and statistical efficiency. \nFirst\, I will introduce an efficient instance-optimal algorithm for control in the presence of adversarial disturbances. Beyond the realm of both stochastic and robust control\, such a data-driven notion of optimality combines worst-case guarantees with a promise of exceptional performance on benign instances. Moving on to prediction\, I will present a computationally and statistically efficient forecasting strategy for latent-state dynamical systems exhibiting long term dependencies\, mitigating the statistical challenge of learning with correlated samples\, and the computational difficulties associated with a non-convex maximum likelihood objective. To conclude\, I will discuss some practically relevant fundamental questions at the intersection of machine learning\, optimization\, and control that have the potential to unlock real progress in downstream applications.
URL:https://seasevents.nmsdev7.com/event/ese-spring-colloquium-provably-robust-algorithms-for-prediction-and-control/
LOCATION:Zoom – Meeting ID 958 3045 4776
CATEGORIES:Seminar,Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
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