Statistical and computational methodology in genetics, cancer biology, metagenomics, and morphometrics; Bayesian methodology for high-dimensional and complex data; Machine learning algorithms for the analysis of massive biological data; Integration of statistical inference with differential geometry and algebraic topology; Stochastic topology; Discrete Hodge theory; Inference in dynamical systems.
Zachary P. Adams et al.
Meta-posterior consistency for the Bayesian inference of metastable system
Shreya Arya et al.
A sheaf-theoretic construction of shape space
Andrea Aveni et al.
Uniform consistency of generalized Fréchet means
Youngsoo Baek et al.
On the frequentist coverage of Bayes posteriors in nonlinear inverse problems
Samuel I. Berchuck et al.
Scalable Bayesian inference for the generalized linear mixed model
Marzieh Eidi et al.
Higher order bipartiteness vs bi-partitioning in simplicial complexes
Henry Kirveslahti et al.
Representing fields without correspondences : the lifted Euler characteristic transform
Eric Roldán Roa et al.
Play My Math : first development cycle of an EdTech tool supporting the teaching and learning of fractions through music in algebraic notation
Andrea Agazzi et al.
Global optimality of Elman-type RNNs in the mean-field regime
Youngsoo Baek et al.
Asymptotics of Bayesian uncertainty estimation in random features regression
Youngsoo Baek et al.
Generalized Bayes approach to inverse problems with model misspecification
Michele Caprio et al.
Concentration inequalities and optimal number of layers for stochastic deep neural networks
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