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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Bandit Algorithms Tor Lattimore and Csaba Szepesv ́ari This is the (free) online edition. The content is the same as the print edition, published by Cambridge University Press, except that minor typos are corrected here. There are also font and other typographical differences that mean the page numbers do not match between the versions. Bandits,
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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