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ORCIDTravis Dick
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2020 – today
- 2024
[j4]Maria-Florina Balcan
, Travis Dick, Tuomas Sandholm, Ellen Vitercik:
Learning to Branch: Generalization Guarantees and Limits of Data-Independent Discretization. J. ACM 71(2): 13:1-13:73 (2024)- [j3]Maria-Florina Balcan, Dan F. DeBlasio, Travis Dick, Carl Kingsford, Tuomas Sandholm, Ellen Vitercik:
How Much Data Is Sufficient to Learn High-Performing Algorithms? J. ACM 71(5): 32:1-32:58 (2024) - [i20]Róbert Istvan Busa-Fekete, Travis Dick, Claudio Gentile, Andrés Muñoz Medina, Adam D. Smith, Marika Swanberg:
Auditing Privacy Mechanisms via Label Inference Attacks. CoRR abs/2406.02797 (2024) - 2023
- [j2]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): 149:1-149:26 (2023) - [c21]Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh:
Subset-Based Instance Optimality in Private Estimation. ICML 2023: 7992-8014 - [c20]Mikhail Khodak, Kareem Amin, Travis Dick, Sergei Vassilvitskii:
Learning-augmented private algorithms for multiple quantile release. ICML 2023: 16344-16376 - [c19]Róbert Busa-Fekete, Heejin Choi, Travis Dick, Claudio Gentile, Andrés Muñoz Medina:
Easy Learning from Label Proportions. NeurIPS 2023 - [c18]Travis Dick, Jennifer Gillenwater, Matthew Joseph:
Better Private Linear Regression Through Better Private Feature Selection. NeurIPS 2023 - [i19]Róbert Istvan Busa-Fekete, Heejin Choi, Travis Dick, Claudio Gentile, Andrés Muñoz Medina:
Easy Learning from Label Proportions. CoRR abs/2302.03115 (2023) - [i18]Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh:
Subset-Based Instance Optimality in Private Estimation. CoRR abs/2303.01262 (2023) - [i17]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 abs/2304.07210 (2023) - [i16]Travis Dick, Jennifer Gillenwater, Matthew Joseph:
Better Private Linear Regression Through Better Private Feature Selection. CoRR abs/2306.00920 (2023) - 2022
- [i15]Kareem Amin, Travis Dick, Mikhail Khodak, Sergei Vassilvitskii:
Private Algorithms with Private Predictions. CoRR abs/2210.11222 (2022) - [i14]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 abs/2211.03128 (2022) - 2021
- [c17]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: 371-389 - [c16]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: 919-932 - 2020
- [j1]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: 211:1-211:21 (2020) - [c15]Dravyansh Sharma, Maria-Florina Balcan, Travis Dick:
Learning piecewise Lipschitz functions in changing environments. AISTATS 2020: 3567-3577 - [c14]Maria-Florina Balcan, Travis Dick, Manuel Lang:
Learning to Link. ICLR 2020 - [c13]Travis Dick, Wesley Pegden, Maria-Florina Balcan:
Semi-bandit Optimization in the Dispersed Setting. UAI 2020: 909-918 - [i13]Avrim Blum, Travis Dick, Naren Manoj, Hongyang Zhang:
Random Smoothing Might be Unable to Certify 𝓁∞ Robustness for High-Dimensional Images. CoRR abs/2002.03517 (2020) - [i12]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 abs/2006.07281 (2020) - [i11]Kaiwen Wang, Travis Dick, Maria-Florina Balcan:
Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning. CoRR abs/2012.10602 (2020)
2010 – 2019
- 2019
- [c12]Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia:
Envy-Free Classification. NeurIPS 2019: 1238-1248 - [c11]Kareem Amin, Travis Dick, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii:
Differentially Private Covariance Estimation. NeurIPS 2019: 14190-14199 - [i10]Maria-Florina Balcan, Travis Dick, Wesley Pegden:
Semi-bandit Optimization in the Dispersed Setting. CoRR abs/1904.09014 (2019) - [i9]Maria-Florina Balcan, Travis Dick, Manuel Lang:
Learning to Link. CoRR abs/1907.00533 (2019) - [i8]Maria-Florina Balcan, Travis Dick, Dravyansh Sharma:
Online optimization of piecewise Lipschitz functions in changing environments. CoRR abs/1907.09137 (2019) - [i7]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 abs/1908.02894 (2019) - 2018
- [c10]Maria-Florina Balcan, Travis Dick, Ellen Vitercik:
Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization. FOCS 2018: 603-614 - [c9]Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik:
Learning to Branch. ICML 2018: 353-362 - [c8]Maria-Florina Balcan, Travis Dick, Colin White:
Data-Driven Clustering via Parameterized Lloyd's Families. NeurIPS 2018: 10664-10674 - [i6]Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik:
Learning to Branch. CoRR abs/1803.10150 (2018) - [i5]Maria-Florina Balcan, Travis Dick, Colin White:
Data-Driven Clustering via Parameterized Lloyd's Families. CoRR abs/1809.06987 (2018) - [i4]Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia:
Envy-Free Classification. CoRR abs/1809.08700 (2018) - 2017
- [c7]Maria-Florina Balcan, Travis Dick, Yishay Mansour:
Label Efficient Learning by Exploiting Multi-Class Output Codes. AAAI 2017: 1735-1741 - [c6]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 - [c5]Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Nina Balcan, Alexander J. Smola:
Data Driven Resource Allocation for Distributed Learning. AISTATS 2017: 662-671 - [c4]Maria-Florina Balcan, Travis Dick, Yingyu Liang, Wenlong Mou, Hongyang Zhang:
Differentially Private Clustering in High-Dimensional Euclidean Spaces. ICML 2017: 322-331 - [i3]Maria-Florina Balcan, Travis Dick, Ellen Vitercik:
Private and Online Optimization of Piecewise Lipschitz Functions. CoRR abs/1711.03091 (2017) - 2015
- [i2]Maria-Florina Balcan, Travis Dick, Yishay Mansour:
On the geometry of output-code multi-class learning. CoRR abs/1511.03225 (2015) - [i1]Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria-Florina Balcan, Alexander J. Smola:
Data Driven Resource Allocation for Distributed Learning. CoRR abs/1512.04848 (2015) - 2014
- [c3]Travis Dick, András György, Csaba Szepesvári:
Online Learning in Markov Decision Processes with Changing Cost Sequences. ICML 2014: 512-520 - 2013
- [c2]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: 1166-1171 - [c1]Travis Dick, Camilo Perez Quintero, Martin Jägersand, Azad Shademan:
Realtime Registration-Based Tracking via Approximate Nearest Neighbour Search. Robotics: Science and Systems 2013
Coauthor Index
aka: Nina Balcan
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last updated on 2024-11-11 22:22 CET by the dblp team
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