I am a fourth year PhD student at Carnegie Mellon University in the Computer Science Department where I am fortunate to be advised by Nina Balcan. Before that, I completed my MSc at the University of Alberta, advised by Richard Sutton and András György.
I am interested in the theoretical foundations of machine learning and their interaction with social constraints including privacy and fairness. My ongoing research focuses on label-efficient learning, distributed learning, differential privacy, and fairness.
Maria-Florina Balcan, Travis Dick, Ellen Vitercik. Private and Online Optimization of Piecewise Lipschitz Functions. Manuscript.
Maria-Florina Balcan, Travis Dick, Yingyu Liang, Wenlong Mou, Hongyang Zhang. Differentially Private Clustering in High-Dimensional Euclidean Spaces. ICML 2017.
Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria-Florina Balcan, Alex Smola. Data Driven Resource Allocation for Distributed Learning. AIStats 2017.
Maria-Florina Balcan, Travis Dick, Yishay Mansour. Label Efficient Learning by Exploiting Multi-class Output Codes. AAAI 2017.
Travis Dick, András György, Csaba Szepesvári. Online Learning in Markov Decision Processes with Changing Cost Sequences. ICML 2014.
Roshan Shariff, Travis Dick. Lunar Lander: A Continuous-Action Case Study for Policy Gradient Actor Critic Algorithms. RLDM 2013 (poster).
Patrick Pilarski, Travis Dick, Richard Sutton. Real-time Prediction Learning for the Simultaneous Actuation of Multiple Prosthetic Joints. ICORR 2013.
Travis Dick, Camilo Perez, Azad Shademan, Martin Jagersand. Realtime Registration-based Tracking via Approximate Nearest Neighbour Search. RSS 2013.
In the Spring semesters of 2015 and 2016, I was a TA for Introduction to Machine Learning (10-601). In 2015 the course was taught by Nina Balcan and Tom Mitchell, and in 2016 it was taught by Nina Balcan and William Cohen. I received a TA Award from the Machine Learning Department for TAing 10-601 in Spring 2016.