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 building on the theory and practice of Machine Learning to accommodate several modern requirements of learning systems. In particular, I have projects on new requirements stemming from three distinct sources: making the best use of available data, applying learning tools to problems beyond standard prediction, and incorporating social values. 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. NeurIPS 2018.

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

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.

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.

PhD Thesis

I defended my PhD Thesis in May 2019 titled "Machine Learning: Social Values, Data Efficiency, and Beyond Prediction". I was fortunate to have Maria-Florina Balcan, Yishay Mansour, Tom Mitchell, and Ariel Procaccia as my thesis committee.

Workshop Papers

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

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

Talks

Invited Talks
Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization.
  • Talk at the Civil Engineering Machine Learning Seminar at CMU (2019).
  • Graduation Day Talk at Information Theory and Applications Workshop (2019).
  • China Theory Week (2018).
Envy-free Classification
  • Poster at Google Fairness in Machine Learning Workshop (2018).
Label Efficient Learning by Exploiting Multi-class Output Codes
  • Talk at Picky Learners Workshop at ICML (2017). (slides)
Other Talks
Machine Learning: Social Values, Data Efficiency, and Beyond Prediction
  • Talk at University of Pennsylvania CIS Department (2019).
  • Talk at Toyota Technical Institute at Chicago (2019). (slides)
Private Covariance Estimation via Iterative Eigenvector Sampling
  • Poster at NeurIPS Privacy Preserving Machine Learning Workshop (2018).
Envy-free Classification
  • Poster at NeurIPS Workshop on Ethical, Social, and Governance Issues in AI (2018).
Data-driven Clustering via Parameterized Lloyd's Families
  • Spotlight Talk at NeurIPS (2018).
Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization
  • Talk at Microsoft Research NYC (2018). (slides)
  • Talk at Google Research NYC (2018).
  • Talk at FOCS (2018). (slides)
Learning to Branch
  • Poster presentation at ICML (2018).
Differentially Private Algorithm Configuration
  • Poster Presentation at Privacy in Machine Learning and AI Workshop at ICML (2018).
  • Poster Presentation at Private and Secure Machine Learning Workshop at ICML (2017).
Data Driven Resource Allocation for Distributed Learning
  • AI Lunch Seminar at CMU (2017).
  • Talk at AAAI Distributed Machine Learning Workshop (2017). (slides)
Label Efficient Learning by Exploiting Multi-class Output Codes
  • Poster Presentation at AAAI (2017). (poster)
  • Talk at ICML Data Efficient Machine Learning Workshop (2016). (short video)
  • Theory Lunch Seminar at CMU (2016). (video)
Online Learning in Markov Decision Processes with Changing Cost Sequences
  • Talk at ICML (2014).
  • Poster Presentation at RLDM (2013).

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.