SEPO: Selecting by pointing as an intuitive human-robot command interface. - Camilo Perez Quintero; Romeo Tatsambon Fomena; Azad Shademan; Nina Wolleb; Travis Dick; Martin Jägersand (ICRA, 2013)

Realtime Registration-Based Tracking via Approximate Nearest Neighbour Search. - Travis Dick; Camilo Perez Quintero; Martin Jägersand; Azad Shademan (Robotics: Science and Systems, 2013)

Online Learning in Markov Decision Processes with Changing Cost Sequences. - Travis Dick; András György 001; Csaba Szepesvári (ICML, 2014)

On the geometry of output-code multi-class learning. - Maria-Florina Balcan; Travis Dick; Yishay Mansour (CoRR, 2015)

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

Data Driven Resource Allocation for Distributed Learning. - Travis Dick; Mu Li 003; Venkata Krishna Pillutla; Colin White; Maria-Florina Balcan; Alexander J. Smola (AAAI Workshops, 2017)

Differentially Private Clustering in High-Dimensional Euclidean Spaces. - Maria-Florina Balcan; Travis Dick; Yingyu Liang; Wenlong Mou; Hongyang Zhang 001 (ICML, 2017)

Private and Online Optimization of Piecewise Lipschitz Functions. - Maria-Florina Balcan; Travis Dick; Ellen Vitercik (CoRR, 2017)

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

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

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

Differentially Private Covariance Estimation. - Kareem Amin 002; Travis Dick; Alex Kulesza; Andres Muñoz Medina; Sergei Vassilvitskii (NeurIPS, 2019)

Envy-Free Classification. - Maria-Florina Balcan; Travis Dick; Ritesh Noothigattu; Ariel D. Procaccia (NeurIPS, 2019)

Online optimization of piecewise Lipschitz functions in changing environments. - Maria-Florina Balcan; Travis Dick; Dravyansh Sharma (CoRR, 2019)

Learning piecewise Lipschitz functions in changing environments. - Dravyansh Sharma; Maria-Florina Balcan; Travis Dick (AISTATS, 2020)

Learning to Link. - Maria-Florina Balcan; Travis Dick; Manuel Lang (ICLR, 2020)

Semi-bandit Optimization in the Dispersed Setting. - Travis Dick; Wesley Pegden; Maria-Florina Balcan (UAI, 2020)

Random Smoothing Might be Unable to Certify 𝓁∞ Robustness for High-Dimensional Images. - Avrim Blum; Travis Dick; Naren Manoj; Hongyang Zhang 001 (CoRR, 2020)

Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning. - Kaiwen Wang; Travis Dick; Maria-Florina Balcan (CoRR, 2020)

Random Smoothing Might be Unable to Certify L∞ Robustness for High-Dimensional Images. - Avrim Blum; Travis Dick; Naren Manoj; Hongyang Zhang 001 (J. Mach. Learn. Res., 2020)

Algorithms and Learning for Fair Portfolio Design. - Emily Diana; Travis Dick; Hadi Elzayn; Michael Kearns; Aaron Roth 001; Zachary Schutzman; Saeed Sharifi-Malvajerdi; Juba Ziani (EC, 2021)

How much data is sufficient to learn high-performing algorithms? generalization guarantees for data-driven algorithm design. - Maria-Florina Balcan; Dan F. DeBlasio; Travis Dick; Carl Kingsford; Tuomas Sandholm; Ellen Vitercik (STOC, 2021)

Private Algorithms with Private Predictions. - Kareem Amin 002; Travis Dick; Mikhail Khodak; Sergei Vassilvitskii (CoRR, 2022)

Confidence-Ranked Reconstruction of Census Microdata from Published Statistics. - Travis Dick; Cynthia Dwork; Michael Kearns; Terrance Liu; Aaron Roth 001; Giuseppe Vietri; Zhiwei Steven Wu (CoRR, 2022)

Subset-Based Instance Optimality in Private Estimation. - Travis Dick; Alex Kulesza; Ziteng Sun; Ananda Theertha Suresh (ICML, 2023)

Learning-augmented private algorithms for multiple quantile release. - Mikhail Khodak; Kareem Amin 002; Travis Dick; Sergei Vassilvitskii (ICML, 2023)

Easy Learning from Label Proportions. - Róbert Busa-Fekete; Heejin Choi; Travis Dick; Claudio Gentile; Andrés Muñoz Medina (NeurIPS, 2023)

Better Private Linear Regression Through Better Private Feature Selection. - Travis Dick; Jennifer Gillenwater; Matthew Joseph (NeurIPS, 2023)

Measuring Re-identification Risk. - CJ Carey; Travis Dick; Alessandro Epasto; Adel Javanmard; Josh Karlin; Shankar Kumar; Andres Muñoz Medina; Vahab Mirrokni; Gabriel Henrique Nunes; Sergei Vassilvitskii; Peilin Zhong (Proc. ACM Manag. Data, 2023)

Auditing Privacy Mechanisms via Label Inference Attacks. - Róbert Busa-Fekete; Travis Dick; Claudio Gentile; Andrés Muñoz Medina; Adam Smith 006; Marika Swanberg (NeurIPS, 2024)

Learning to Branch: Generalization Guarantees and Limits of Data-Independent Discretization. - Maria-Florina Balcan; Travis Dick; Tuomas Sandholm; Ellen Vitercik (J. ACM, 2024)

How Much Data Is Sufficient to Learn High-Performing Algorithms? - Maria-Florina Balcan; Dan F. DeBlasio; Travis Dick; Carl Kingsford; Tuomas Sandholm; Ellen Vitercik (J. ACM, 2024)

Nearly Optimal Sample Complexity for Learning with Label Proportions. - Róbert Busa-Fekete; Travis Dick; Claudio Gentile; Haim Kaplan; Tomer Koren; Uri Stemmer (CoRR, 2025)

Differentially Private Synthetic Data Release for Topics API Outputs. - Travis Dick; Alessandro Epasto; Adel Javanmard; Josh Karlin; Andrés Muñoz Medina; Vahab Mirrokni; Sergei Vassilvitskii; Peilin Zhong (CoRR, 2025)