A phase transition for finding needles in nonlinear haystacks with LASSO artificial neural networks [0.03%]
LASSO人工神经网络在非线性大规模稀疏回归问题中的相变现象
Xiaoyu Ma,Sylvain Sardy,Nick Hengartner et al.
Xiaoyu Ma et al.
To fit sparse linear associations, a LASSO sparsity inducing penalty with a single hyperparameter provably allows to recover the important features (needles) with high probability in certain regimes even if the sample size is smaller than t...
[Formula: see text] VAE: a stochastic process prior for Bayesian deep learning with MCMC [0.03%]
[公式见正文中] VAE：贝叶斯深度学习中使用MCMC的随机过程先验
Swapnil Mishra,Seth Flaxman,Tresnia Berah et al.
Swapnil Mishra et al.
Stochastic processes provide a mathematically elegant way to model complex data. In theory, they provide flexible priors over function classes that can encode a wide range of interesting assumptions. However, in practice efficient inference...
Geometry-informed irreversible perturbations for accelerated convergence of Langevin dynamics [0.03%]
几何引导的不可逆扰动加速Langevin动力学收敛
Benjamin J Zhang,Youssef M Marzouk,Konstantinos Spiliopoulos
Benjamin J Zhang
We introduce a novel geometry-informed irreversible perturbation that accelerates convergence of the Langevin algorithm for Bayesian computation. It is well documented that there exist perturbations to the Langevin dynamics that preserve it...
Default risk prediction and feature extraction using a penalized deep neural network [0.03%]
一种罚函数在深度神经网络模型中的应用：信用违约预测及特征选择问题研究
Cunjie Lin,Nan Qiao,Wenli Zhang et al.
Cunjie Lin et al.
Online peer-to-peer lending platforms provide loans directly from lenders to borrowers without passing through traditional financial institutions. For lenders on these platforms to avoid loss, it is crucial that they accurately assess defau...
A Joint estimation approach to sparse additive ordinary differential equations [0.03%]
稀疏加性常微分方程的联合估计方法
Nan Zhang,Muye Nanshan,Jiguo Cao
Nan Zhang
Ordinary differential equations (ODEs) are widely used to characterize the dynamics of complex systems in real applications. In this article, we propose a novel joint estimation approach for generalized sparse additive ODEs where observatio...
Luca Merlo,Antonello Maruotti,Lea Petrella et al.
Luca Merlo et al.
This paper develops a quantile hidden semi-Markov regression to jointly estimate multiple quantiles for the analysis of multivariate time series. The approach is based upon the Multivariate Asymmetric Laplace (MAL) distribution, which allow...
Stéphane Girard,Gilles Stupfler,Antoine Usseglio-Carleve
Stéphane Girard
Expectiles induce a law-invariant risk measure that has recently gained popularity in actuarial and financial risk management applications. Unlike quantiles or the quantile-based Expected Shortfall, the expectile risk measure is coherent an...
Parsimonious hidden Markov models for matrix-variate longitudinal data [0.03%]
稀疏隐马尔可夫模型在矩阵型纵向数据中的应用
Salvatore D Tomarchio,Antonio Punzo,Antonello Maruotti
Salvatore D Tomarchio
Hidden Markov models (HMMs) have been extensively used in the univariate and multivariate literature. However, there has been an increased interest in the analysis of matrix-variate data over the recent years. In this manuscript we introduc...
Inference on high-dimensional implicit dynamic models using a guided intermediate resampling filter [0.03%]
基于引导中间重抽样滤波的高维隐式动态模型推理方法研究
Joonha Park,Edward L Ionides
Joonha Park
We propose a method for inference on moderately high-dimensional, nonlinear, non-Gaussian, partially observed Markov process models for which the transition density is not analytically tractable. Markov processes with intractable transition...
Lucas Kook,Beate Sick,Peter Bühlmann
Lucas Kook
Prediction models often fail if train and test data do not stem from the same distribution. Out-of-distribution (OOD) generalization to unseen, perturbed test data is a desirable but difficult-to-achieve property for prediction models and i...