Services running in a data center frequently rely on RPCs to child services (e.g. storage, cache, authentication), and their latency depends crucially on latencies of those RPCs. However, even though service latency often comes exclusively from the time spent inside remote calls, it is difficult to determine parent latency since multithreading and asynchronous RPCs lead to complex and non-linea...

A recently proposed mutual information based algorithm for decomposing data into least dependent components (MILCA) is applied to spectral analysis, namely to blind recovery of concentrations and pure spectra from their linear mixtures. The algorithm is based on precise estimates of mutual information between measured spectra, which allows to assess and make use of actual statistical dependenci...

Recovering signals that has a sparse representation from a given set of linear measurements has been a major topic of research in recent years. Most of the work dealing with this subject focus on the reconstruction of the signal’s representation as the means to recover the signal itself. This approach forces the dictionary to be of low-coherence and with no linear dependencies between its colum...

Most compression algorithms used in storage systems today are based on an increasingly outmoded sequential processing model. Systems wishing to decompress blocks out-of-order or in parallel must reset the compressor’s state before each block, reducing adaptiveness and limiting compression ratios. To remedy this situation, we present Non-Linear Compression, a novel compression model enabling sys...

Recovering signals that have sparse representations under a given dictionary from a set of linear measurements got much attention in the recent decade. However, most of the work has focused on recovering the signal’s representation, forcing the dictionary to be incoherent and with no linear dependencies between small sets of its columns. A series of recent papers show that such dependencies can...

Representing interactions between variables in large data sets in an understandable way is usually important and hard task. This article presents a methodology how a linear dependency structure between variables can be constructed from multivariate data. The dependencies between the variables are specified by multiple linear regression models. A sparse regression algorithm and bootstrap based r...

We present a method for simultaneously learning linear models of multiple high dimensional data sets and the dependencies between them. For example, we learn asymmetrically coupled linear models for the faces of two di erent people and show how these models can be used to animate one face given a video sequence of the other. We pose the problem as a form of Asymmetric Coupled Component Analysis...

The statistical dependencies that independent component analysis (ICA) cannot remove often provide rich information beyond the linear independent components. It would thus be very useful to estimate the dependency structure from data. While such models have been proposed, they have usually concentrated on higher-order correlations such as energy (square) correlations. Yet linear correlations ar...