I'm a PhD student at University of Alberta in Edmonton, Alberta, Canada. I have a BS in Physics and an MS in Computer Science both from Indiana University Bloomington. My current PhD work is focused on reinforcement learning, and specifically in understanding how agents may perceive their world. I focus primarily on prediction making, but have been known to dabble in control from time-to-time. My active research interests include: predictions as a component in intelligence (both artificial and biological), off-policy prediction and policy evaluation, deep learning and resulting learned representations in the reinforcement learning context, and discovery or attention of important abstractions (described as predictions) through interaction.
Recently Published papers
General Value Function Networks. Matthew Schlegel, Andrew Jacobsen, Zaheer Abbas, Andrew Patterson, Adam White, Martha White . Journal of Artificial Intelligence Research, 2021.
Importance Resampling for Off-policy Prediction. Matthew Schlegel, Wesley Chung, Daniel Graves, Jian Qian, Martha White . Advances in Neural Information Processing Systems 32, 2019.
Meta-descent for Online, Continual Prediction. Andrew Jacobsen, Matthew Schlegel, Cameron Linke, Thomas Degris, Adam White, Martha White . AAAI Conference on Artificial Intelligence, 2019.
Context-dependent upper-confidence bounds for directed exploration. Raksha Kumaraswamy, Matthew Schlegel, Adam White, Martha White . Advances in Neural Information Processing Systems, 2018.
Adapting kernel representations online using submodular maximization. Matthew Schlegel, Yangchen Pan, Jiecao Chen, Martha White . Proceedings of the 34th International Conference on Machine Learning-Volume 70, 2017.