Travis Dick

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 39

Top Topics

Differentially Private

Markov Decision Process

Top Venues

Publications

Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik
Learning to Branch: Generalization Guarantees and Limits of Data-Independent Discretization. J. ACM 71 (2) (2024)
Róbert Istvan Busa-Fekete, Travis Dick, Claudio Gentile, Andrés Muñoz Medina, Adam D. Smith, Marika Swanberg
Auditing Privacy Mechanisms via Label Inference Attacks. CoRR (2024)
Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh
Subset-Based Instance Optimality in Private Estimation. CoRR (2023)
Travis Dick, Jennifer Gillenwater, Matthew Joseph
Better Private Linear Regression Through Better Private Feature Selection. NeurIPS (2023)
CJ Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andres Muñoz Medina, Vahab Mirrokni, Gabriel Henrique Nunes, Sergei Vassilvitskii, Peilin Zhong
Measuring Re-identification Risk. Proc. ACM Manag. Data 1 (2) (2023)
CJ Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andrés Muñoz Medina, Vahab Mirrokni, Gabriel Henrique Nunes, Sergei Vassilvitskii, Peilin Zhong
Measuring Re-identification Risk. CoRR (2023)
Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh
Subset-Based Instance Optimality in Private Estimation. ICML (2023)
Róbert Busa-Fekete, Heejin Choi, Travis Dick, Claudio Gentile, Andrés Muñoz Medina
Easy Learning from Label Proportions. NeurIPS (2023)
Mikhail Khodak, Kareem Amin, Travis Dick, Sergei Vassilvitskii
Learning-augmented private algorithms for multiple quantile release. ICML (2023)
Róbert Istvan Busa-Fekete, Heejin Choi, Travis Dick, Claudio Gentile, Andrés Muñoz Medina
Easy Learning from Label Proportions. CoRR (2023)
Travis Dick, Jennifer Gillenwater, Matthew Joseph
Better Private Linear Regression Through Better Private Feature Selection. CoRR (2023)
Kareem Amin, Travis Dick, Mikhail Khodak, Sergei Vassilvitskii
Private Algorithms with Private Predictions. CoRR (2022)
Travis Dick, Cynthia Dwork, Michael Kearns, Terrance Liu, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu
Confidence-Ranked Reconstruction of Census Microdata from Published Statistics. CoRR (2022)
Maria-Florina Balcan, Dan F. DeBlasio, Travis Dick, Carl Kingsford, Tuomas Sandholm, Ellen Vitercik
How much data is sufficient to learn high-performing algorithms? generalization guarantees for data-driven algorithm design. STOC (2021)
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)
Avrim Blum, Travis Dick, Naren Manoj, Hongyang Zhang
Robustness for High-Dimensional Images. CoRR (2020)
Travis Dick, Wesley Pegden, Maria-Florina Balcan
Semi-bandit Optimization in the Dispersed Setting. UAI (2020)
Avrim Blum, Travis Dick, Naren Manoj, Hongyang Zhang
Random Smoothing Might be Unable to Certify L∞ Robustness for High-Dimensional Images. J. Mach. Learn. Res. 21 (2020)
Dravyansh Sharma, Maria-Florina Balcan, Travis Dick
Learning piecewise Lipschitz functions in changing environments. AISTATS (2020)
Kaiwen Wang, Travis Dick, Maria-Florina Balcan
Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning. CoRR (2020)
Maria-Florina Balcan, Travis Dick, Manuel Lang
Learning to Link. ICLR (2020)
Emily Diana, Travis Dick, Hadi Elzayn, Michael J. Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, Juba Ziani
Algorithms and Learning for Fair Portfolio Design. CoRR (2020)
Maria-Florina Balcan, Travis Dick, Wesley Pegden
Semi-bandit Optimization in the Dispersed Setting. CoRR (2019)
Maria-Florina Balcan, Dan F. DeBlasio, Travis Dick, Carl Kingsford, Tuomas Sandholm, Ellen Vitercik
How much data is sufficient to learn high-performing algorithms? CoRR (2019)
Kareem Amin, Travis Dick, Alex Kulesza, Andres Muñoz 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, Manuel Lang
Learning to Link. CoRR (2019)
Maria-Florina Balcan, Travis Dick, Dravyansh Sharma
Online optimization of piecewise Lipschitz functions in changing environments. CoRR (2019)
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. CoRR (2018)
Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik
Learning to Branch. ICML (2018)
Maria-Florina Balcan, Travis Dick, Colin White
Data-Driven Clustering via Parameterized Lloyd's Families. NeurIPS (2018)
Maria-Florina Balcan, Travis Dick, Colin White
Data-Driven Clustering via Parameterized Lloyd's Families. CoRR (2018)
Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia
Envy-Free Classification. CoRR (2018)
Maria-Florina Balcan, Travis Dick, Yishay Mansour
Label Efficient Learning by Exploiting Multi-Class Output Codes. AAAI (2017)
Maria-Florina Balcan, Travis Dick, Ellen Vitercik
Private and Online Optimization of Piecewise Lipschitz Functions. CoRR (2017)
Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria-Florina Balcan, Alexander J. Smola
Data Driven Resource Allocation for Distributed Learning. AAAI Workshops (2017)
Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Nina Balcan, Alexander J. Smola
Data Driven Resource Allocation for Distributed Learning. AISTATS (2017)
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, Alexander J. Smola
Data Driven Resource Allocation for Distributed Learning. CoRR (2015)
Maria-Florina Balcan, Travis Dick, Yishay Mansour
On the geometry of output-code multi-class learning. CoRR (2015)
Travis Dick, András György, Csaba Szepesvári
Online Learning in Markov Decision Processes with Changing Cost Sequences. ICML (2014)
Travis Dick, Camilo Perez Quintero, Martin Jägersand, Azad Shademan
Realtime Registration-Based Tracking via Approximate Nearest Neighbour Search. Robotics: Science and Systems (2013)
Camilo Perez Quintero, Romeo Tatsambon Fomena, Azad Shademan, Nina Wolleb, Travis Dick, Martin Jägersand
SEPO: Selecting by pointing as an intuitive human-robot command interface. ICRA (2013)