We present a case study on malware detection--a binary classification problem on byte sequences where classifier evasion is a well-established threat model.

We propose contrastive coding to learn shared, dense image representations, referredtoasCoMIRs (ContrastiveMultimodal ImageRepresentations).

Semantic segmentation has witnessed tremendous progress due to the proposal of various advanced network architectures.

We prove a Chernoff-type bound for sums of matrix-valued random variables sampled viaaregular (aperiodic and irreducible) finite Markovchain.

Clustering is a fundamental problem in unsupervised learning with several practical applications. In clustering, one is interested in partitioning elements into different groups (i.e.

There has been much interest in recent years in the problem of dueling bandits, where on each round the learner plays a pair of arms and receives as feedback the outcome of a relative pairwise comparison between them.