Federated learning is a useful framework for centralized learning from distributed data under practical considerations of heterogeneity, asynchrony, and privacy. Federated architectures are frequently deployed in deep learning settings, which
Parameter Sensitivity Analysis of the SparTen High Performance Sparse Tensor Decomposition Software: Extended Analysis. (arXiv:2012.01520v1 [math.NA])
Tensor decomposition models play an increasingly important role in modern data science applications. One problem of particular interest is fitting a low-rank Canonical Polyadic (CP) tensor decomposition model when the
Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression. (arXiv:2012.01536v1 [cs.LG])
The Shapley value solution concept from cooperative game theory has become popular for interpreting ML models, but efficiently estimating Shapley values remains challenging, particularly in the model-agnostic setting. We revisit
Robust functional principal components for sparse longitudinal data. (arXiv:2012.01540v1 [stat.ME])
In this paper we review existing methods for robust functional principal component analysis (FPCA) and propose a new method for FPCA that can be applied to longitudinal data where only
Regularization via Adaptive Pairwise Label Smoothing. (arXiv:2012.01559v1 [cs.LG])
Label Smoothing (LS) is an effective regularizer to improve the generalization of state-of-the-art deep models. For each training sample the LS strategy smooths the one-hot encoded training signal by distributing
Graph Neural Networks for Improved El Ni\~no Forecasting. (arXiv:2012.01598v1 [cs.LG])
Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Ni\~no-Southern Oscillation (ENSO). However, current deep learning models are based on convolutional neural networks which
Multiply robust estimation of causal effects under principal ignorability. (arXiv:2012.01615v1 [stat.ME])
Causal inference concerns not only the average effect of the treatment on the outcome but also the underlying mechanism through an intermediate variable of interest. Principal stratification characterizes such mechanism
Matrix Completion Methods for the Total Electron Content Video Reconstruction. (arXiv:2012.01618v1 [stat.AP])
The total electron content (TEC) maps can be used to estimate the signal delay of GPS due to the ionospheric electron content between a receiver and satellite. This delay can
brolgar: An R package to BRowse Over Longitudinal Data Graphically and Analytically in R. (arXiv:2012.01619v1 [stat.AP])
Longitudinal (panel) data provide the opportunity to examine temporal patterns of individuals, because measurements are collected on the same person at different, and often irregular, time points. The data is
Forecast with Forecasts: Diversity Matters. (arXiv:2012.01643v1 [stat.ME])
Forecast combination has been widely applied in the last few decades to improve forecast accuracy. In recent years, the idea of using time series features to construct forecast combination model
