About the Course and Prerequisites

The standard approach to machine learning uses a training set of labeled examples to learn a prediction rule that will predict the labels of new examples. Collecting such training sets can be expensive and time-consuming. This course will explore methods that leverage already-collected data to guide future measurements, in a closed loop, to best serve the task at hand. We focus on two paradigms: i) in pure-exploration we desire algorithms that identify or learn a good model using as few measurements as possible (e.g., classification, drug discovery, science), and ii) in regret minimization we desire algorithms that balance taking measurements to learn a model with taking measurements to exploit the model to obtain high reward outcomes (e.g., content recommendation, medical treatment design, ad-serving).

The literature on adaptive methods for machine learning has exploded in the past few years and can be overwhelming. This course will classify different adaptive machine learning problems by characteristics such as the hypothesis space, the available actions, the measurement model, and the available side information. We will identify general adaptive strategies and cover common proof techniques.

Tentative list of topics (a (!) indicates that the topic may not be covered):

For more, see the resources in the class materials.

Prerequisites: The course will assume introductory machine learning (e.g., CSE 546) and maturity in topics like linear algebra, statistics, and calculus. The course will be analysis heavy, with a focus on methods that work well in practice.

Class materials

There will not be a textbook for the course. Our discussion will be guided by papers, monographs, and lecture notes that are available online. An incomplete list that will grow:

Other resources that may be useful:

Discussion Forum and Email Communication

There will be a Slack channel (first day of class). This is your first resource for questions. For private or confidential questions email the instructor.

Grading and Evaluation

Your grade will be based on scribing and potentially presenting a subset of a single lecture (e.g., a technical proof in the work following an overview by the instructor). You are expected to prepare for the lecture you are assigned to well before the lecture occurs by reading and understanding not only the assigned papers but supporting materials and concepts as well (i.e. if the paper uses a lemma from a different paper, you should understand that lemma and where it comes from). Scribing a lecture means summarizing the assigned papers and the lecture itself (with main theorems and proofs) as well as how this work fits into the context of the class so far and why it matters (e.g., linear bandits is a multi-armed bandit game with a given feature vector, a setting with applications to X, Y, Z). You are expected to put several hours into preparing these notes, a mere summary of what was presented in lecture without context or applications is unacceptable. You must come to the instructor's office hours (or by appointment) days preceding the lecture to discuss the plan for that lecture presentation and notes. Instructions will be sent out on how to submit your preferences over lectures -- the plan is to have each innermost bullet point be a single lecture.

Scribe notes should be prepared using the Latex template. The final notes should be turned in within a few days following lecture with the understanding that the majority of the notes should be completed before lecture.