CSCI 659 Lecture 2 Fall 2022 Instructor: Haipeng Luo 1 A General Algorithmic Framework In the last lecture, we discussed a special case of OCO, that is, the expert problem, and the classical Hedge algorithm for this problem. For a general OCO problem, how should we design a no-regret algorithm? (Recall the OCO setting: each […]
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Parametric Bandits: The Generalized Linear Case Sarah Filippi Telecom ParisTech et CNRS Paris, France Aure ́lien Garivier Telecom ParisTech et CNRS Paris, France We consider structured multi-armed bandit problems based on the Generalized Linear Model (GLM) framework of statistics. For these bandits, we propose a new algorithm, called GLM-UCB. We derive finite time, high probability
Course overview and logistics CS861: Theoretical Foundations of Machine Learning Kirthi Kandasamy University of Wisconsin – Madison Fall 2023 September 6, 2023 Machine learning is popular nowadays! “A breakthrough in ML will be worth 10 Microsofts” – Bill Gates “ML is the new internet” “AI will be the best or worst thing ever for
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Understanding Machine Learning: From Theory to Algorithms ⃝c 2014 by Shai Shalev-Shwartz and Shai Ben-David Published 2014 by Cambridge University Press. This copy is for personal use only. Not for distribution. Do not post. Please link to: http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning Please note: This copy is almost, but not entirely, identical to the printed version of the book.
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CS861: Theoretical Foundations of Machine Learning Lecture 1 – 10/30/2023 University of Wisconsin–Madison, Fall 2023 Lecture 24: EXP3, Lower Bounds for adversarial bandits Lecturer: Kirthevasan Kandasamy Scribed by: Bo-Hsun Chen, Zexuan Sun Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside this class only
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CS861: Theoretical Foundations of Machine Learning Lecture 15 – 09/10/2023 University of Wisconsin–Madison, Fall 2023 Lecture 15: Density estimation, Lower bounds for prediction problems Lecturer: Kirthevasan Kandasamy Scribed by: Haoyue Bai, Zexuan Sun Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside this class
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CS861: Theoretical Foundations of Machine Learning Lecture 21 – 10/23/2023 University of Wisconsin–Madison, Fall 2023 Lecture 21: Martingale concentration and structured bandits Lecturer: Kirthevasan Kandasamy Scribed by: Zhifeng Chen, Xinyan Wang Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside this class only with
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CS861: Theoretical Foundations of Machine Learning Lecture 1 – 09/13/2023 University of Wisconsin–Madison, Fall 2023 Lecture 04: Rademacher Complexity & Growth Function Lecturer: Kirthevasan Kandasamy Scribed by: Yixuan Zhang, Elliot Pickens Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside this class only with
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CS861: Theoretical Foundations of Machine Learning Lecture 1-2 – 09/06-08/2023 University of Wisconsin–Madison, Fall 2023 Lecture 01 & 02: PAC Learning Lecturer: Kirthevasan Kandasamy Scribed by: Albert Dorador, Michael Harding Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside this class only with the
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CS861: Theoretical Foundations of Machine Learning Lecture 18 – 10/16/2023 University of Wisconsin–Madison, Fall 2023 Lecture 18: Proof for UCB (cont’d), K-armed Bandit Lower Bound Lecturer: Kirthevasan Kandasamy Scribed by: Michael Harding and Congwei Yang Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside
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