Causal inference studies whether the presence of a variable influences a...
The analysis of large-scale time-series network data, such as social med...
In this paper, we introduce a novel approach to multi-graph embedding ca...
In this paper we propose a novel and computationally efficient method to...
In this paper we propose a lightning fast graph embedding method called ...
We present a simple generative model in which spectral graph embedding f...
A number of universally consistent dependence measures have been recentl...
Distance correlation has gained much recent attention in the statistics ...
Testing independence and testing equality of distributions are two tight...
A fundamental problem in statistical data analysis is testing whether tw...
We introduce hyppo, a unified library for performing multivariate hypoth...
With the increase in the amount of data in many fields, a method to
cons...
Information-theoretic quantities, such as mutual information and conditi...
Identifying statistically significant dependency between variables is a ...
The sparse representation classifier (SRC) is shown to work well for ima...
Decision forests are popular tools for classification and regression. Th...
Distance-based methods, also called "energy statistics", are leading met...
Graph classification and regression have wide applications in a variety ...
Understanding and developing a correlation measure that can detect gener...
Determining whether certain properties are related to other properties i...
The sparse representation classifier (SRC) proposed in Wright et al. (20...
Matching datasets of multiple modalities has become an important task in...
For random graphs distributed according to stochastic blockmodels, a spe...
For multiple multivariate data sets, we derive conditions under which
Ge...
Suppose that two large, multi-dimensional data sets are each noisy
measu...