Travis Dick

53 papers A* 18A 3Journal 29Unranked 3
YearRankTypeTitle / Venue / Authors
2026 J jnl
AI-rithmetic.
CoRR
Alex Bie, Travis Dick, Alex Kulesza, Prabhakar Raghavan, Vinod Raman, Sergei Vassilvitskii
2026 J jnl
Missing Mass for Differentially Private Domain Discovery.
CoRR
Travis Dick, Matthew Joseph, Vinod Raman
2025 — conf
Differentially Private Synthetic Data Release for Topics API Outputs.
KDD (2)
Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Andrés Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong
2025 J jnl
Differentially Private Synthetic Data Release for Topics API Outputs.
CoRR
Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Andrés Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong
2025 A* conf
Nearly Optimal Sample Complexity for Learning with Label Proportions.
ICML
Róbert Istvan Busa-Fekete, Travis Dick, Claudio Gentile, Haim Kaplan, Tomer Koren, Uri Stemmer
2025 J jnl
Nearly Optimal Sample Complexity for Learning with Label Proportions.
CoRR
Róbert Busa-Fekete, Travis Dick, Claudio Gentile, Haim Kaplan, Tomer Koren, Uri Stemmer
2025 J jnl
Optimal Learning from Label Proportions with General Loss Functions.
CoRR
Lorne Applebaum, Travis Dick, Claudio Gentile, Haim Kaplan, Tomer Koren
2024 A* conf
Auditing Privacy Mechanisms via Label Inference Attacks.
NeurIPS
Róbert Busa-Fekete, Travis Dick, Claudio Gentile, Andrés Muñoz Medina, Adam Smith, Marika Swanberg
2024 J jnl
Auditing Privacy Mechanisms via Label Inference Attacks.
CoRR
Róbert Istvan Busa-Fekete, Travis Dick, Claudio Gentile, Andrés Muñoz Medina, Adam Smith, Marika Swanberg
2024 J jnl
How Much Data Is Sufficient to Learn High-Performing Algorithms?
J. ACM
Maria-Florina Balcan, Dan F. DeBlasio, Travis Dick, Carl Kingsford, Tuomas Sandholm, Ellen Vitercik
2024 J jnl
Learning to Branch: Generalization Guarantees and Limits of Data-Independent Discretization.
J. ACM
Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik
2023 A* conf
Better Private Linear Regression Through Better Private Feature Selection.
NeurIPS
Travis Dick, Jennifer Gillenwater, Matthew Joseph
2023 J jnl
Better Private Linear Regression Through Better Private Feature Selection.
CoRR
Travis Dick, Jennifer Gillenwater, Matthew Joseph
2023 A* conf
Easy Learning from Label Proportions.
NeurIPS
Róbert Busa-Fekete, Heejin Choi, Travis Dick, Claudio Gentile, Andrés Muñoz Medina
2023 J jnl
Easy Learning from Label Proportions.
CoRR
Róbert Istvan Busa-Fekete, Heejin Choi, Travis Dick, Claudio Gentile, Andrés Muñoz Medina
2023 A* conf
Learning-augmented private algorithms for multiple quantile release.
ICML
Mikhail Khodak, Kareem Amin, Travis Dick, Sergei Vassilvitskii
2023 J jnl
Measuring Re-identification Risk.
CoRR
CJ Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andrés Muñoz Medina, Vahab Mirrokni, Gabriel Henrique Nunes, Sergei Vassilvitskii, Peilin Zhong
2023 J jnl
Measuring Re-identification Risk.
Proc. ACM Manag. Data
CJ Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andres Muñoz Medina, Vahab Mirrokni, Gabriel Henrique Nunes, Sergei Vassilvitskii, Peilin Zhong
2023 A* conf
Subset-Based Instance Optimality in Private Estimation.
ICML
Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh
2023 J jnl
Subset-Based Instance Optimality in Private Estimation.
CoRR
Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh
2022 J jnl
Confidence-Ranked Reconstruction of Census Microdata from Published Statistics.
CoRR
Travis Dick, Cynthia Dwork, Michael Kearns, Terrance Liu, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu
2022 J jnl
Private Algorithms with Private Predictions.
CoRR
Kareem Amin, Travis Dick, Mikhail Khodak, Sergei Vassilvitskii
2021 A* conf
Algorithms and Learning for Fair Portfolio Design.
EC
Emily Diana, Travis Dick, Hadi Elzayn, Michael Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, Juba Ziani
2021 A* conf
How much data is sufficient to learn high-performing algorithms? generalization guarantees for data-driven algorithm design.
STOC
Maria-Florina Balcan, Dan F. DeBlasio, Travis Dick, Carl Kingsford, Tuomas Sandholm, Ellen Vitercik
2020 J jnl
Algorithms and Learning for Fair Portfolio Design.
CoRR
Emily Diana, Travis Dick, Hadi Elzayn, Michael J. Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, Juba Ziani
2020 A conf
Learning piecewise Lipschitz functions in changing environments.
AISTATS
Dravyansh Sharma, Maria-Florina Balcan, Travis Dick
2020 A* conf
Learning to Link.
ICLR
Maria-Florina Balcan, Travis Dick, Manuel Lang
2020 J jnl
Random Smoothing Might be Unable to Certify L∞ Robustness for High-Dimensional Images.
J. Mach. Learn. Res.
Avrim Blum, Travis Dick, Naren Manoj, Hongyang Zhang
2020 J jnl
Random Smoothing Might be Unable to Certify 𝓁
CoRR
Avrim Blum, Travis Dick, Naren Manoj, Hongyang Zhang
2020 J jnl
Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning.
CoRR
Kaiwen Wang, Travis Dick, Maria-Florina Balcan
2020 A conf
Semi-bandit Optimization in the Dispersed Setting.
UAI
Travis Dick, Wesley Pegden, Maria-Florina Balcan
2019 A* conf
Differentially Private Covariance Estimation.
NeurIPS
Kareem Amin, Travis Dick, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii
2019 A* conf
Envy-Free Classification.
NeurIPS
Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia
2019 J jnl
How much data is sufficient to learn high-performing algorithms?
CoRR
Maria-Florina Balcan, Dan F. DeBlasio, Travis Dick, Carl Kingsford, Tuomas Sandholm, Ellen Vitercik
2019 J jnl
Learning to Link.
CoRR
Maria-Florina Balcan, Travis Dick, Manuel Lang
2019 J jnl
Online optimization of piecewise Lipschitz functions in changing environments.
CoRR
Maria-Florina Balcan, Travis Dick, Dravyansh Sharma
2019 J jnl
Semi-bandit Optimization in the Dispersed Setting.
CoRR
Maria-Florina Balcan, Travis Dick, Wesley Pegden
2018 A* conf
Data-Driven Clustering via Parameterized Lloyd's Families.
NeurIPS
Maria-Florina Balcan, Travis Dick, Colin White
2018 J jnl
Data-Driven Clustering via Parameterized Lloyd's Families.
CoRR
Maria-Florina Balcan, Travis Dick, Colin White
2018 A* conf
Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization.
FOCS
Maria-Florina Balcan, Travis Dick, Ellen Vitercik
2018 J jnl
Envy-Free Classification.
CoRR
Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia
2018 A* conf
Learning to Branch.
ICML
Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik
2018 J jnl
Learning to Branch.
CoRR
Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik
2017 — conf
Data Driven Resource Allocation for Distributed Learning.
AAAI Workshops
Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria-Florina Balcan, Alexander J. Smola
2017 A conf
Data Driven Resource Allocation for Distributed Learning.
AISTATS
Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Nina Balcan, Alexander J. Smola
2017 A* conf
Differentially Private Clustering in High-Dimensional Euclidean Spaces.
ICML
Maria-Florina Balcan, Travis Dick, Yingyu Liang, Wenlong Mou, Hongyang Zhang
2017 A* conf
Label Efficient Learning by Exploiting Multi-Class Output Codes.
AAAI
Maria-Florina Balcan, Travis Dick, Yishay Mansour
2017 J jnl
Private and Online Optimization of Piecewise Lipschitz Functions.
CoRR
Maria-Florina Balcan, Travis Dick, Ellen Vitercik
2015 J jnl
Data Driven Resource Allocation for Distributed Learning.
CoRR
Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria-Florina Balcan, Alexander J. Smola
2015 J jnl
On the geometry of output-code multi-class learning.
CoRR
Maria-Florina Balcan, Travis Dick, Yishay Mansour
2014 A* conf
Online Learning in Markov Decision Processes with Changing Cost Sequences.
ICML
Travis Dick, András György, Csaba Szepesvári
2013 — conf
Realtime Registration-Based Tracking via Approximate Nearest Neighbour Search.
Robotics: Science and Systems
Travis Dick, Camilo Perez Quintero, Martin Jägersand, Azad Shademan
2013 A* conf
SEPO: Selecting by pointing as an intuitive human-robot command interface.
ICRA
Camilo Perez Quintero, Romeo Tatsambon Fomena, Azad Shademan, Nina Wolleb, Travis Dick, Martin Jägersand