Recent empirical results show that deep networks can approximate solutions to high dimensional PDEs, seemingly escaping the curse of dimensionality. However many open questions remain regarding the theoretical basis for
Two-Stage Framework for Seasonal Time Series Forecasting. (arXiv:2103.02144v1 [cs.LG])
Seasonal time series Forecasting remains a challenging problem due to the long-term dependency from seasonality. In this paper, we propose a two-stage framework to forecast univariate seasonal time series. The
Ridge-penalized adaptive Mantel test and its application in imaging genetics. (arXiv:2103.02156v1 [stat.ME])
We propose a ridge-penalized adaptive Mantel test (AdaMant) for evaluating the association of two high-dimensional sets of features. By introducing a ridge penalty, AdaMant tests the association across many metrics
To Deconvolve, or Not to Deconvolve: Inferences of Neuronal Activities using Calcium Imaging Data. (arXiv:2103.02163v1 [q-bio.NC])
With the increasing popularity of calcium imaging data in neuroscience research, methods for analyzing calcium trace data are critical to address various questions. The observed calcium traces are either analyzed
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series. (arXiv:2103.02164v1 [cs.LG])
Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging applications. However,
Parsimonious Inference. (arXiv:2103.02165v1 [stat.ML])
Bayesian inference provides a uniquely rigorous approach to obtain principled justification for uncertainty in predictions, yet it is difficult to articulate suitably general prior belief in the machine learning context,
Minimax MSE Bounds and Nonlinear VAR Prewhitening for Long-Run Variance Estimation Under Nonstationarity. (arXiv:2103.02235v1 [econ.EM])
We establish new mean-squared error (MSE) bounds for long-run variance (LRV) estimation, valid for both stationary and nonstationary sequences that are sharper than previously established. The key element to construct
Meta-Learning with Variational Bayes. (arXiv:2103.02265v1 [cs.LG])
The field of meta-learning seeks to improve the ability of today’s machine learning systems to adapt efficiently to small amounts of data. Typically this is accomplished by training a system
Asymptotic approximation of the likelihood of stationary determinantal point processes. (arXiv:2103.02310v1 [math.ST])
Continuous determinantal point processes (DPPs) are a class of repulsive point processes on $\mathbb{R}^d$ with many statistical applications. Although an explicit expression of their density is known, this expression is
Discussion of ‘Estimating time-varying causal excursion effect in mobile health with binary outcomes’ by T. Qian et al. (arXiv:2103.02323v1 [stat.ME])
We discuss the recent paper on “excursion effect” by T. Qian et al. (2020). We show that the methods presented have close relationships to others in the literature, in particular
