Interactive Machine Learning: Algorithms and Theory

CMPSCI 691E, Fall 2016

Akshay Krishnamurthy

When: Tuesday 4-5pm
Where: CS 142
Office Hours: By appointment

Overview: Interactive machine learning involves an algorithm or an agent making decisions about data collection, contrasting starkly with traditional learning paradigms. Interactive data collection often enables learning with significantly less data, and it is critical in a number of applications including personalized recommendation, medical diagnosis, and dialogue systems. This seminar will focus on the design and analysis of interactive learning algorithms for settings including active learning, bandits, reinforcement learning, and adaptive sensing. We will cover foundational and contemporary papers, with an emphasis on algorithmic design principles as well as understanding and proving performance guarantees.

Requirements (3 units): Students enrolled in the 3 unit version of the course are required to present one paper in lecture and complete a small research project in interactive learning, in addition to reading the papers, attending class, and engaging in discussion. Presentation of a paper should include proofs of the main results. Research projects can comprise original research or a literature survey on any topic related to interactive learning. Project guidelines are available here.

Requirements (1 unit): Students enrolled in the 1 unit version of the course are expected to read the assigned papers, attend class, and engage in discussions.

Tentative Syllabus
Date Lecture Topics Readings Presenter
9/6 Stochastic Bandits Akshay
9/13 Online Learning and Adversarial Bandits Soumyabrata Pal
9/20 Special Bandit Topics Ryan McKenna
9/27 Parametric Bandits Akshay
10/4 Contextual Bandits Raj Kumar Maity
10/11 NO CLASS
10/18 Model-Based PAC Reinforcement Learning Tengyang Xie
10/25 Reinforcement Learning Regret Bounds Akshay
11/1 Model-Free PAC Reinforcement Learning Akshay
11/8 Selective Sampling/Parametric Active Learning Haw-Shiuan Chang
11/15 Active Learning Survey Craig Greenberg
11/22 NO CLASS
11/29 Agnostic Active Learning Yuqing Xing
12/13 Agnostic Active Learning Akshay
Additional Reading: Some other papers that may be of interest are listed here.