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Jul 29, 2017
Methodology

Bayesian Regression Tree Ensembles that Adapt to Smoothness and Spars…

Ensembles of decision trees are a useful tool for obtaining for obtaining flexible estimates of regression functions. Examples of these methods inclu…
Mar 07, 2016
Computer Vision
Differential Geometry

Elastic Functional Coding of Riemannian Trajectories

Visual observations of dynamic phenomena, such as human actions, are often represented as sequences of smoothly-varying features . In cases where the…
Apr 02, 2015
Machine Learning

Bayesian Clustering of Shapes of Curves

Unsupervised clustering of curves according to their shapes is an important problem with broad scientific applications. The existing model-based clus…
Aug 16, 2017
Statistics Theory
Computation
Machine Learning

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.

Frequentist coverage and sup-norm convergence rate in Gaussian proces…

Gaussian process (GP) regression is a powerful interpolation technique due to its flexibility in capturing non-linearity. In this paper, we provide a…
Dec 09, 2016
Methodology

A Divide and Conquer Strategy for High Dimensional Bayesian Factor …

We propose a distributed computing framework, based on a divide and conquer strategy and hierarchical modeling, to accelerate posterior inference for…
Sep 27, 2019
Artificial Intelligence

Playing Atari Ball Games with Hierarchical Reinforcement Learning

Human beings are particularly good at reasoning and inference from just a few examples. When facing new tasks, humans will leverage knowledge and ski…
Feb 11, 2020
Machine Learning

A study of local optima for learning feature interactions using neura…

In many fields such as bioinformatics, high energy physics, power distribution, etc., it is desirable to learn non-linear models where a small number…
Apr 24, 2017
Applications

Trend and Variable-Phase Seasonality Estimation from Functional Data

The problem of estimating trend and seasonal variation in time-series data has been studied over several decades, although mostly using single time s…
Dec 08, 2012
Computation
Statistics Theory

Generative Models for Functional Data using Phase and Amplitude Sep…

Constructing generative models for functional observations is an important task in statistical functional analysis. In general, functional data conta…
Apr 14, 2014
Computer Vision

Face Detection with a 3D Model

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…
Feb 03, 2017
Other Statistics

Nonparametric Spherical Regression Using Diffeomorphic Mappings

Spherical regression explores relationships between variables on spherical domains. We develop a nonparametric model that uses a diffeomorphic map fr…
Mar 30, 2018
Machine Learning

Online Regression with Model Selection

Online learning algorithms have a wide variety of applications in large scale machine learning problems due to their low computational and memory req…
Sep 16, 2017
Machine Learning

Relevant Ensemble of Trees

Tree ensembles are flexible predictive models that can capture relevant variables and to some extent their interactions in a compact and interpretabl…
Sep 14, 2018
Machine Learning

Are screening methods useful in feature selection? An empirical study

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…
Nov 04, 2011
Statistics Theory

Anisotropic function estimation using multi-bandwidth Gaussian proces…

In nonparametric regression problems involving multiple predictors, there is typically interest in estimating an anisotropic multivariate regression …
Sep 05, 2019
Computation and Language

Multi-Granularity Self-Attention for Neural Machine Translation

Current state-of-the-art neural machine translation (NMT) uses a deep multi-head self-attention network with no explicit phrase information. However,…
Sep 04, 2019
Computation and Language

Towards Better Modeling Hierarchical Structure for Self-Attention wit…

Recent studies have shown that a hybrid of self-attention networks (SANs) and recurrent neural networks (RNNs) outperforms both individual architectu…
Mar 30, 2018
Machine Learning

A Novel Framework for Online Supervised Learning with Feature Selecti…

Current online learning methods suffer issues such as lower convergence rates and limited capability to recover the support of the true features comp…
Feb 11, 2020
Machine Learning

Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neur…

Artificial Neural Networks form the basis of very powerful learning methods. It has been observed that a naive application of fully connected neural …
Jun 14, 2010
Methodology
Machine Learning
Computation

Outlier Detection Using Nonconvex Penalized Regression

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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