3 accepted papers
High-quality machine learning models are dependent on access to high-quality training data. When the data are not already available, it is tedious and costly to obtain them. Data markets help with identifying valuable training data: model consumers pay to train a model, the market uses that budget t…
We study the matrix completion problem under joint differential privacy and develop a non-convex low-rank matrix factorization-based method for solving it. Our method comes with strong privacy and utility guarantees, has a linear convergence rate, and is more scalable than the best-known alternative…
We consider the problem of estimating the difference between two functional undirected graphical models with shared structures. In many applications, data are naturally regarded as high-dimensional random function vectors rather than multivariate scalars. For example, electroencephalography (EEG) da…