Travis Dick

GHC 6008

tdick@cs.cmu.edu

About Me

I am a fifth 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 extending the practice and theory of Machine Learning to accommodate new requirements derived from its application in the real world. In particular, I am interested in incorporating social values such as privacy and fairness into machine learning, data efficiency for large-scale multi-class learning, and applying tools from machine learning to problems beyond standard prediction. I have also worked on actor-critic methods for Reinforcement Learning, Online Learning, and distributed learning.

Here is my cv.

Manuscripts

Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia. Envy-free Classification.

Conference Papers

Maria-Florina Balcan, Travis Dick, Colin White. Data-Driven Clustering via Parameterized Lloyd's Families. NIPS 2018.

Maria-Florina Balcan, Travis Dick, Ellen Vitercik. Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization. FOCS 2018.

Related links: Slides for talk at FOCS

Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik. Learning to Branch. ICML 2018.

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.

Related links: Slides for talk at AAAI Distributed Machine Learning Workshop

Maria-Florina Balcan, Travis Dick, Yishay Mansour. Label Efficient Learning by Exploiting Multi-class Output Codes. AAAI 2017.

Related links:

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.

Workshop Papers

Kareem Amin, Travis Dick, Alex Kulesza, Andres Medina, Sergei Vassilvitskii. Private Covariance Estimation via Iterative Eigenvector Sampling. NIPS Privacy Preserving Machine Learning Workshop 2018.

Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia. Envy-free Classification. NIPS Workshop on Ethical, Social, and Governance Issues in AI 2018.

Maria-Florina Balcan, Travis Dick, Ellen Vitercik. Dispersion for Private Optimization of Piecewise Lipschitz Functions. ICML Privacy in Machine Learning and AI Workshop 2018.

Maria-Florina Balcan, Travis Dick, Ellen Vitercik. Dispersion for Private Optimization of Piecewise Lipschitz Functions. ICML Privacy in Machine Learning and AI Workshop 2018.

Maria-Florina Balcan, Travis Dick, Ellen Vitercik. Differentially Private Algorithm Configuration. ICML Private and Secure Machine Learning Workshop 2017.

Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria-Florina Balcan, Alex Smola. Data Driven Resource Allocation for Distributed Learning. AAAI Distributed Machine Learning Workshop 2017.

Maria-Florina Balcan, Travis Dick, Yishay Mansour. On the Geometry of Output-code Multi-class Learning. ICML Data Efficient Machine Learning Workshop 2016.

Teaching

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.