68 citations · 163 across the 18 of their papers we have counts for
4 papers · 2 filters
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…
Semi-bandit Optimization in the Dispersed Setting
Maria-Florina Balcan, Travis Dick, Wesley Pegden
The goal of data-driven algorithm design is to obtain high-performing algorithms for specific application domains using machine learning and data. Across many fields in AI, science…