arXiv Statistics @arxiv_stats@qoto.org

High-Dimensional Penalized Bernstein Support Vector Machines. (arXiv:2303.09066v1 [stat.ML]) http://arxiv.org/abs/2303.09066

On a fundamental problem in the analysis of cancer registry data. (arXiv:2303.09141v1 [stat.ME]) http://arxiv.org/abs/2303.09141

Bayesian Generalization Error in Linear Neural Networks with Concept Bottleneck Structure and Multitask Formulation. (arXiv:2303.09154v1 [stat.ML]) http://arxiv.org/abs/2303.09154

Identifiability Results for Multimodal Contrastive Learning. (arXiv:2303.09166v1 [cs.LG]) http://arxiv.org/abs/2303.09166

Limit Shape of the Generalized Inverse Gaussian-Poisson Distribution. (arXiv:2303.08139v1 [math.ST]) http://arxiv.org/abs/2303.08139

Adaptive Testing for High-dimensional Data. (arXiv:2303.08197v1 [math.ST]) http://arxiv.org/abs/2303.08197

Spatial causal inference in the presence of unmeasured confounding and interference. (arXiv:2303.08218v1 [stat.ME]) http://arxiv.org/abs/2303.08218

Bayesian Beta-Bernoulli Process Sparse Coding with Deep Neural Networks. (arXiv:2303.08230v1 [cs.LG]) http://arxiv.org/abs/2303.08230

Optimal Sampling Designs for Multi-dimensional Streaming Time Series with Application to Power Grid Sensor Data. (arXiv:2303.08242v1 [stat.ML]) http://arxiv.org/abs/2303.08242

Reimagining Doctoral Training in Statistics: Is There a Role for a Professional Doctorate?. (arXiv:2303.08282v1 [stat.OT]) http://arxiv.org/abs/2303.08282

Estimating Parameters of Large CTMP from Single Trajectory with Application to Stochastic Network Epidemics Models. (arXiv:2303.08323v1 [stat.AP]) http://arxiv.org/abs/2303.08323

Latent space approaches to aggregate network data. (arXiv:2303.08338v1 [cs.SI]) http://arxiv.org/abs/2303.08338

Policy Gradient Converges to the Globally Optimal Policy for Nearly Linear-Quadratic Regulators. (arXiv:2303.08431v1 [cs.LG]) http://arxiv.org/abs/2303.08431

The Benefits of Mixup for Feature Learning. (arXiv:2303.08433v1 [cs.LG]) http://arxiv.org/abs/2303.08433

Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion. (arXiv:2303.07392v1 [stat.ML]) http://arxiv.org/abs/2303.07392

Tuning support vector machines and boosted trees using optimization algorithms. (arXiv:2303.07400v1 [stat.ML]) http://arxiv.org/abs/2303.07400

General Loss Functions Lead to (Approximate) Interpolation in High Dimensions. (arXiv:2303.07475v1 [stat.ML]) http://arxiv.org/abs/2303.07475

A non-parametric proportional risk model to assess a treatment effect in time-to-event data. (arXiv:2303.07479v1 [stat.ME]) http://arxiv.org/abs/2303.07479

Using VAEs to Learn Latent Variables: Observations on Applications in cryo-EM. (arXiv:2303.07487v1 [stat.ML]) http://arxiv.org/abs/2303.07487

Comparing the Robustness of Simple Network Scale-Up Method (NSUM) Estimators. (arXiv:2303.07490v1 [stat.ME]) http://arxiv.org/abs/2303.07490