
Charles ZhengStanford University | SU · Department of Statistics
Charles Zheng
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Publications (9)
In many scientific applications, the mutual information I(X; Y) between the measurement Y and the relevant attributes X of a system can provide a general measure of the utility of the measurement for a wide variety of downstream applications. However, most approaches for estimating I(X; Y) require joint observations of X and Y. Here we explore the...
The difficulty of multi-class classification generally increases with the number of classes. Using data from a subset of the classes, can we predict how well a classifier will scale with an increased number of classes? Under the assumption that the classes are sampled exchangeably, and under the assumption that the classifier is generative (e.g. QD...
Estimating the mutual information $I(X; Y)$ based on observations becomes statistically infeasible in high dimensions without some kind of assumption or prior. One approach is to assume a parametric joint distribution on $(X, Y)$, but in many applications, such a strong modeling assumption cannot be justified. Alternatively, one can estimate the mu...
Approximate Bayesian Computation (ABC) methods are used to approximate
posterior distributions in models with unknown or computationally intractable
likelihoods. Both the accuracy and computational efficiency of ABC depend on
the choice of summary statistic, but outside of special cases where the optimal
summary statistics are known, it is unclear...
Diffusion-weighted MR imaging (DWI) is the only method we currently have to
measure connections between different parts of the human brain in vivo. To
elucidate the structure of these connections, algorithms for tracking bundles
of axonal fibers through the subcortical white matter rely on local estimates
of the fiber orientation distribution funct...
Diffusion-weighted magnetic resonance imaging (DWI) and fiber tractography
are the only methods to measure the structure of the white matter in the living
human brain. The diffusion signal has been modelled as the combined
contribution from many individual fascicles of nerve fibers passing through
each location in the white matter. Typically, this...
Motivated by the problem of identifying correlations between genes or
features of two related biological systems, we propose a model of \emph{feature
selection} in which only a subset of the predictors $X_t$ are dependent on the
multidimensional variate $Y$, and the remainder of the predictors constitute a
"noise set" $X_u$ independent of $Y$. Usin...
The Protein–RNA Interface Database (PRIDB) is a comprehensive database of protein–RNA interfaces extracted from complexes
in the Protein Data Bank (PDB). It is designed to facilitate detailed analyses of individual protein–RNA complexes and their
interfaces, in addition to automated generation of user-defined data sets of protein–RNA interfaces for...