Ensembles of decision trees are a useful tool for obtaining for obtaining flexible estimates of regression functions. Examples of these methods inclu…

Visual observations of dynamic phenomena, such as human actions, are often represented as sequences of smoothly-varying features . In cases where the…

Unsupervised clustering of curves according to their shapes is an important problem with broad scientific applications. The existing model-based clus…

In this section, we provide proofs of the results in the paper. For notational brevity, we drop λ from the subscript of ∥⋅∥λ within the proofs; all instances of ∥⋅∥ refer to the RKHS norm (for the equivalent kernel) ∥⋅∥λ.

Gaussian process (GP) regression is a powerful interpolation technique due to its flexibility in capturing non-linearity. In this paper, we provide a…

We propose a distributed computing framework, based on a divide and conquer strategy and hierarchical modeling, to accelerate posterior inference for…

Human beings are particularly good at reasoning and inference from just a few examples. When facing new tasks, humans will leverage knowledge and ski…

In many fields such as bioinformatics, high energy physics, power distribution, etc., it is desirable to learn non-linear models where a small number…

The problem of estimating trend and seasonal variation in time-series data has been studied over several decades, although mostly using single time s…

Constructing generative models for functional observations is an important task in statistical functional analysis. In general, functional data conta…

This paper presents a part-based face detection approach where the spatial relationship between the face parts is represented by a hidden 3D model wi…

Spherical regression explores relationships between variables on spherical domains. We develop a nonparametric model that uses a diffeomorphic map fr…

Online learning algorithms have a wide variety of applications in large scale machine learning problems due to their low computational and memory req…

Tree ensembles are flexible predictive models that can capture relevant variables and to some extent their interactions in a compact and interpretabl…

Filter or screening methods are often used as a preprocessing step for reducing the number of variables used by a learning algorithm in obtaining a c…

In nonparametric regression problems involving multiple predictors, there is typically interest in estimating an anisotropic multivariate regression …

Current state-of-the-art neural machine translation (NMT) uses a deep multi-head self-attention network with no explicit phrase information. However,…

Recent studies have shown that a hybrid of self-attention networks (SANs) and recurrent neural networks (RNNs) outperforms both individual architectu…

Current online learning methods suffer issues such as lower convergence rates and limited capability to recover the support of the true features comp…

Artificial Neural Networks form the basis of very powerful learning methods. It has been observed that a naive application of fully connected neural …

This paper studies the outlier detection problem from the point of view of penalized regressions. Our regression model adds one mean shift parameter …

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