15 citations · 19 across the 2 of their papers we have counts for
9 papers
Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning
Kaiwen Wang, Travis Dick, Maria-Florina Balcan
This paper introduces the first provably accurate algorithms for differentially private, top-down decision tree learning in the distributed setting (Balcan et al., 2012). We propos…
Algorithms and Learning for Fair Portfolio Design
Emily Diana, Travis Dick, Hadi Elzayn +5
We consider a variation on the classical finance problem of optimal portfolio design. In our setting, a large population of consumers is drawn from some distribution over risk tole…
Random Smoothing Might be Unable to Certify Robustness for High-Dimensional Images
Avrim Blum, Travis Dick, Naren Manoj +1
We show a hardness result for random smoothing to achieve certified adversarial robustness against attacks in the ball of radius when . Although random smoothing…
How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design
Maria-Florina Balcan, Dan DeBlasio, Travis Dick +3
Algorithms often have tunable parameters that impact performance metrics such as runtime and solution quality. For many algorithms used in practice, no parameter settings admit mea…
Learning piecewise Lipschitz functions in changing environments
Maria-Florina Balcan, Travis Dick, Dravyansh Sharma
Optimization in the presence of sharp (non-Lipschitz), unpredictable (w.r.t. time and amount) changes is a challenging and largely unexplored problem of great significance. We cons…
Learning to Link
Maria-Florina Balcan, Travis Dick, Manuel Lang
Clustering is an important part of many modern data analysis pipelines, including network analysis and data retrieval. There are many different clustering algorithms developed by v…