EECS 492 – Homework 1 (Fall 2024) Due: September 27, 2024 at 11:59pm September 17, 2024 Logistics Submission Guidelines IMPORTANT NOTE: It is essential that you read carefully the policies in the Syllabus section on Homework. It discusses what counts as late and how late penalties will be applied. Please also be sure you have …
CS/ECE/STAT-861: Theoretical Foundations of Machine Learning University of Wisconsin–Madison, Fall 2023 Homework 2. Due 10/27/2023, 11.00 am Instructions: 1. Homework is due at 11 am on the due date. Please hand over your homework at the beginning of class. Please see the course website for the policy on late submission. 2. I recommend that you …
STAT 861: Theoretical Foundations of Machine Learning University of Wisconsin–Ma Read More »
CS/ECE/STAT-861: Theoretical Foundations of Machine Learning University of Wisconsin–Madison, Fall 2023 Homework 0. Due 9/15/2023, 11.00 am Instructions: 1. Homework is due at 11 am on the due date. Please hand over your homework at the beginning of class. Please see the course website for the policy on late submission. 2. I recommend that you …
STAT 861: Theoretical Foundations of Machine Learning University of Wisconsin–Ma Read More »
CS/ECE/STAT-861: Theoretical Foundations of Machine Learning University of Wisconsin–Madison, Fall 2023 Homework 3. Due 11/08/2023, 11.00 am Instructions: 1. Homework is due at 11 am on the due date. Please hand over your homework at the beginning of class. Please see the course website for the policy on late submissions. 2. I recommend that you …
STAT 861: Theoretical Foundations of Machine Learning University of Wisconsin–Ma Read More »
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 …
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 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 …
CS861: Theoretical Foundations of Machine Learning Lecture 18 10 16 2023 Unive Read More »
CS861: Theoretical Foundations of Machine Learning Lecture 19 – 18/10/2023 University of Wisconsin–Madison, Fall 2023 Lecture 19: K–armed bandit lower bounds, generalized linear bandits Lecturer: Kirthevasan Kandasamy Scribed by: Alex Clinton, Yamin Zhou 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 17 – 10/13/2023 University of Wisconsin–Madison, Fall 2023 Lecture 17: K-armed bandits, the UCB algorithm Lecturer: Kirthevasan Kandasamy Scribed by: Ransheng Guan, Yamin Zhou Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside this class only with …
CS861: Theoretical Foundations of Machine Learning Lecture 17 10 13 2023 Unive Read More »