arXiv Statistics @arxiv_stats@qoto.org

Scaling Gaussian Processes with Derivative Information Using Variational Inference. (arXiv:2107.04061v1 [cs.LG]) http://arxiv.org/abs/2107.04061

Ensembles of Randomized NNs for Pattern-based Time Series Forecasting. (arXiv:2107.04091v1 [cs.LG]) http://arxiv.org/abs/2107.04091

OCDE: Odds Conditional Density Estimator. (arXiv:2107.04118v1 [stat.ME]) http://arxiv.org/abs/2107.04118

Many Objective Bayesian Optimization. (arXiv:2107.04126v1 [stat.ML]) http://arxiv.org/abs/2107.04126

Diagonal Nonlinear Transformations Preserve Structure in Covariance and Precision Matrices. (arXiv:2107.04136v1 [math.ST]) http://arxiv.org/abs/2107.04136

MCMC Variational Inference via Uncorrected Hamiltonian Annealing. (arXiv:2107.04150v1 [cs.LG]) http://arxiv.org/abs/2107.04150

On the Variance of the Fisher Information for Deep Learning. (arXiv:2107.04205v1 [cs.LG]) http://arxiv.org/abs/2107.04205

From Many to One: Consensus Inference in a MIP. (arXiv:2107.04208v1 [stat.AP]) http://arxiv.org/abs/2107.04208

Two Sample Test for Extrinsic Antimeans on Planar Kendall Shape Spaces with an Application to Medical Imaging. (arXiv:2107.04230v1 [math.ST]) http://arxiv.org/abs/2107.04230

ENNS: Variable Selection, Regression, Classification and Deep Neural Network for High-Dimensional Data. (arXiv:2107.03430v1 [stat.ME]) http://arxiv.org/abs/2107.03430

Bayesian model-based clustering for multiple network data. (arXiv:2107.03431v1 [stat.AP]) http://arxiv.org/abs/2107.03431

In-Network Learning: Distributed Training and Inference in Networks. (arXiv:2107.03433v1 [cs.LG]) http://arxiv.org/abs/2107.03433

Identifying optimally cost-effective dynamic treatment regimes with a Q-learning approach. (arXiv:2107.03441v1 [stat.ME]) http://arxiv.org/abs/2107.03441

Model Selection for Generic Contextual Bandits. (arXiv:2107.03455v1 [stat.ML]) http://arxiv.org/abs/2107.03455

Uncertainity in Ranking. (arXiv:2107.03459v1 [stat.ME]) http://arxiv.org/abs/2107.03459

Impossibility results for fair representations. (arXiv:2107.03483v1 [cs.LG]) http://arxiv.org/abs/2107.03483

The folded concave Laplacian spectral penalty learns block diagonal sparsity patterns with the strong oracle property. (arXiv:2107.03494v1 [math.ST]) http://arxiv.org/abs/2107.03494

CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation. (arXiv:2107.03502v1 [cs.LG]) http://arxiv.org/abs/2107.03502

The Micro-Randomized Trial for Developing Digital Interventions: Experimental Design and Data Analysis Considerations. (arXiv:2107.03544v1 [stat.AP]) http://arxiv.org/abs/2107.03544

New Methods and Datasets for Group Anomaly Detection From Fundamental Physics. (arXiv:2107.02821v1 [stat.ML]) http://arxiv.org/abs/2107.02821