Michael I. Jordan

TL;DR: This work first parse console logs by combining source code analysis with information retrieval to create composite features, and then analyzes these features using machine learning to detect operational problems to automatically detect system runtime problems.

TL;DR: A novel method of dimensionality reduction for supervised learning problems that requires neither assumptions on the marginal distribution of X, nor a parametric model of the conditional distribution of Y, and establishes a general nonparametric characterization of conditional independence using covariance operators on reproducing kernel Hilbert spaces.

TL;DR: Conditional domain adversarial networks (CDANs) as discussed by the authors are designed with two novel conditioning strategies: multilinear conditioning that captures the cross-covariance between feature representations and classifier predictions to improve the discriminability, and entropy conditioning that controls the uncertainty of classifier prediction to guarantee the transferability.

TL;DR: N nanopore direct RNA-seq is demonstrated, a highly parallel, real-time, single-molecule method that circumvents reverse transcription or amplification steps and enables the direct detection of nucleotide analogs in RNA.

TL;DR: This paper describes a computational framework for integrating and drawing inferences from a collection of genome-wide measurements represented via a kernel function, which defines generalized similarity relationships between pairs of entities, such as genes or proteins.