Travis Dick

According to our database, Travis Dick authored at least 30 papers between 2013 and 2023.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

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

Better Private Linear Regression Through Better Private Feature Selection.

Travis Dick, Jennifer Gillenwater, Matthew Joseph

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Easy Learning from Label Proportions.

Róbert Busa-Fekete, Heejin Choi, Travis Dick, Claudio Gentile, Andrés Muñoz Medina

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning-augmented private algorithms for multiple quantile release.

Mikhail Khodak, Kareem Amin, Travis Dick, Sergei Vassilvitskii

Proceedings of the International Conference on Machine Learning, 2023

Subset-Based Instance Optimality in Private Estimation.

Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh

Proceedings of the International Conference on Machine Learning, 2023

2022

Confidence-Ranked Reconstruction of Census Microdata from Published Statistics.

Travis Dick, Cynthia Dwork, Michael Kearns, Terrance Liu, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu

CoRR, 2022

Private Algorithms with Private Predictions.

Kareem Amin, Travis Dick, Mikhail Khodak, Sergei Vassilvitskii

CoRR, 2022

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

Proceedings of the STOC '21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2021

Algorithms and Learning for Fair Portfolio Design.

Emily Diana, Travis Dick, Hadi Elzayn, Michael Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, Juba Ziani

Proceedings of the EC '21: The 22nd ACM Conference on Economics and Computation, 2021

2020

Random Smoothing Might be Unable to Certify L∞ Robustness for High-Dimensional Images.

Avrim Blum, Travis Dick, Naren Manoj, Hongyang Zhang

J. Mach. Learn. Res., 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

CoRR, 2020

Semi-bandit Optimization in the Dispersed Setting.

Travis Dick, Wesley Pegden, Maria-Florina Balcan

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Learning to Link.

Maria-Florina Balcan, Travis Dick, Manuel Lang

Proceedings of the 8th International Conference on Learning Representations, 2020

Learning piecewise Lipschitz functions in changing environments.

Dravyansh Sharma, Maria-Florina Balcan, Travis Dick

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

How much data is sufficient to learn high-performing algorithms?

Maria-Florina Balcan, Dan F. DeBlasio, Travis Dick, Carl Kingsford, Tuomas Sandholm, Ellen Vitercik

CoRR, 2019

Online optimization of piecewise Lipschitz functions in changing environments.

Maria-Florina Balcan, Travis Dick, Dravyansh Sharma

CoRR, 2019

Envy-Free Classification.

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

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Differentially Private Covariance Estimation.

Kareem Amin, Travis Dick, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Data-Driven Clustering via Parameterized Lloyd's Families.

Maria-Florina Balcan, Travis Dick, Colin White

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning to Branch.

Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik

Proceedings of the 35th International Conference on Machine Learning, 2018

Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization.

Maria-Florina Balcan, Travis Dick, Ellen Vitercik

Proceedings of the 59th IEEE Annual Symposium on Foundations of Computer Science, 2018

2017

Private and Online Optimization of Piecewise Lipschitz Functions.

Maria-Florina Balcan, Travis Dick, Ellen Vitercik

CoRR, 2017

Differentially Private Clustering in High-Dimensional Euclidean Spaces.

Maria-Florina Balcan, Travis Dick, Yingyu Liang, Wenlong Mou, Hongyang Zhang

Proceedings of the 34th International Conference on Machine Learning, 2017

Data Driven Resource Allocation for Distributed Learning.

Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria-Florina Balcan, Alexander J. Smola

Proceedings of the Workshops of the The Thirty-First AAAI Conference on Artificial Intelligence, 2017

Label Efficient Learning by Exploiting Multi-Class Output Codes.

Maria-Florina Balcan, Travis Dick, Yishay Mansour

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2015

On the geometry of output-code multi-class learning.

Maria-Florina Balcan, Travis Dick, Yishay Mansour

CoRR, 2015

2014

Online Learning in Markov Decision Processes with Changing Cost Sequences.

Travis Dick, András György, Csaba Szepesvári

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Realtime Registration-Based Tracking via Approximate Nearest Neighbour Search.

Travis Dick, Camilo Perez Quintero, Martin Jägersand, Azad Shademan

Proceedings of the Robotics: Science and Systems IX, Technische Universität Berlin, Berlin, Germany, June 24, 2013

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

Proceedings of the 2013 IEEE International Conference on Robotics and Automation, 2013