This paper addresses the problem of grouping the data points sampled from a union of
multiple subspaces in the presence of outliers. Information theoretic objective functions are …

We introduce a definition of the notion of compressibility for infinite deterministic and iid
random sequences which is based on the asymptotic behavior of truncated subsequences …

This paper presents a novel noise-robust graph-based semi-supervised learning algorithm
to deal with the challenging problem of semi-supervised learning with noisy initial labels …

This paper presents a large-scale sparse coding algorithm to deal with the challenging
problem of noise-robust semi-supervised learning over very large data with only few noisy …

In many real-world applications of supervised learning, only a limited number of labeled
examples are available because the cost of obtaining high-quality examples is high. Even …

Executions of large-scale simulations that generate enormous amounts of data are now
ubiquitous across the sciences. Low-level differential equations are used to drive the …

Learning with limited supervision presents a major challenge to machine learning systems
in practice. Fortunately, various types of extra information exist in real-world problems …