About Me
I am a research scientist at Google. Before that, I was a Warren Center Postdoctoral Fellow in the Department of Computer and Information Science at UPenn hosted by Michael Kearns and Aaron Roth. I completed my PhD at Carnegie Mellon University in the Computer Science Department advised by Nina Balcan and my MSc at the University of Alberta, advised by Richard Sutton and András György. I also had the pleasure of being a visiting scientist at the Toyota Technical Institute at Chicago in the summer of 2019.
Publications
Travis Dick, Jennifer Gillenwater, Matthew Joseph. Better Private Linear Regression Through Better Private Feature Selection. NeurIPS 2023.
Robert Istvan Busa-Fekete, Heejin Choi, Travis Dick, Claudio Gentile, Andres Munoz Medina. Easy Learning from Label Proportions. NeurIPS 2023.
Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh. Subset-Based Instance Optimality in Private Estimation. ICML 2023.
Mikhail Khodak, Kareem Amin, Travis Dick, Sergei Vassilvitskii. Learning-augmented private algorithms for multiple quantile release. ICML 2023.
CJ Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andres Munoz Medina, Vahab Mirrokni, Gabriel Henrique Nunes, Sergei Vassilvitskii, Peilin Zhong. Measuring Re-identification Risk. SIGMOD 2023.
Travis Dick, Cynthia Dwork, Michael Kearns, Terrance Liu, Aaron Roth, Giuseppe Vietri, and Zhiwei Steven Wu. Confidence-Ranked Reconstruction of Census Microdata from Published Statistics. PNAS 2023.
Emily Diana, Travis Dick, Hadi Elzayn, Michael Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, Juba Ziani. Algorithms and Learning for Fair Portfolio Design. EC 2021.
Maria-Florina Balcan, Dan DeBlasio, Travis Dick, Carl Kingsford, Tuomas Sandholm, Ellen Vitercik. How much data is sufficient to learn high-performing algorithms? STOC 2021.
Avrim Blum, Travis Dick, Naren Manoj, Hongyang Zhang. Random Smoothing Might be Unable to Certify L∞ Robustness for High-Dimensional Images. Journal of Machine Learning Research 2020.
Maria-Florina Balcan, Travis Dick, Wesley Pegden. Semi-bandit Optimization in the Dispersed Setting UAI 2020.
Maria-Florina Balcan, Travis Dick, Dravyansh Sharma. Learning piecewise Lipschitz functions in changing environments . AISTATS 2020.
Maria-Florina Balcan, Travis Dick, Manuel Lang. Learning To Link. ICLR 2020.
Kareem Amin, Travis Dick, Alex Kulesza, Andres Munoz Medina, Sergei Vassilvitskii. Differentially Private Covariance Estimation. NeurIPS 2019.
Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia. Envy-free Classification. NeurIPS 2019.
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
Maria-Florina Balcan, Travis Dick, Kai Wen Wang. Scalable and provably accurate algorithms for differentially private distributed decision tree learning. AAAI Workshop on Privacy-Preserving Artificial Intelligence 2020.
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.