Dick, Travis

22 publications

ICML 2025 Nearly Optimal Sample Complexity for Learning with Label Proportions Robert Istvan Busa-Fekete, Travis Dick, Claudio Gentile, Haim Kaplan, Tomer Koren, Uri Stemmer

NeurIPS 2025 Private Set Union with Multiple Contributions Travis Dick, Haim Kaplan, Alex Kulesza, Uri Stemmer, Ziteng Sun, Ananda Theertha Suresh

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

NeurIPSW 2023 A New Framework for Measuring Re-Identification Risk Cj Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andres Munoz Medina, Vahab Mirrokni, Gabriel Nunes, Sergei Vassilvitskii, Peilin Zhong

NeurIPSW 2023 A Unified Analysis of Label Inference Attacks Andres Munoz Medina, Travis Dick, Claudio Gentile, Robert Istvan Busa-Fekete, Marika Swanberg

NeurIPS 2023 Better Private Linear Regression Through Better Private Feature Selection Travis Dick, Jennifer Gillenwater, Matthew Joseph

NeurIPS 2023 Easy Learning from Label Proportions Róbert Busa-Fekete, Heejin Choi, Travis Dick, Claudio Gentile, Andres Munoz Medina

ICML 2023 Learning-Augmented Private Algorithms for Multiple Quantile Release Mikhail Khodak, Kareem Amin, Travis Dick, Sergei Vassilvitskii

ICMLW 2023 Learning-Augmented Private Algorithms for Multiple Quantile Release Mikhail Khodak, Kareem Amin, Travis Dick, Sergei Vassilvitskii

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

AISTATS 2020 Learning Piecewise Lipschitz Functions in Changing Environments Dravyansh Sharma, Maria-Florina Balcan, Travis Dick

ICLR 2020 Learning to Link Maria-Florina Balcan, Travis Dick, Manuel Lang

JMLR 2020 Random Smoothing Might Be Unable to Certify $\ell_\infty$ Robustness for High-Dimensional Images Avrim Blum, Travis Dick, Naren Manoj, Hongyang Zhang

UAI 2020 Semi-Bandit Optimization in the Dispersed Setting Maria-Florina Balcan, Travis Dick, Wesley Pegden

NeurIPS 2019 Differentially Private Covariance Estimation Kareem Amin, Travis Dick, Alex Kulesza, Andres Munoz, Sergei Vassilvitskii

NeurIPS 2019 Envy-Free Classification Maria-Florina F Balcan, Travis Dick, Ritesh Noothigattu, Ariel D Procaccia

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

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

AISTATS 2017 Data Driven Resource Allocation for Distributed Learning Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Nina Balcan, Alexander J. Smola

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

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

ICML 2014 Online Learning in Markov Decision Processes with Changing Cost Sequences Travis Dick, Andras Gyorgy, Csaba Szepesvari