Quantile regression is a method of fundamental importance. How to efficiently conduct quantile regression for a large dataset on a distributed system is of great importance. We show that the popula…
This paper reviews the novel concept of a controllable variational autoencoder (ControlVAE), discusses its parameter tuning to meet application needs, derives its key analytic properties, and offer…
We collected and cleaned a large dataset on publications in statistics. The dataset consists of the co-author relationships and citation relationships of 83, 331 articles published in 36 representa…
In the emerging field of materials informatics, a fundamental task is to identify physicochemically meaningful descriptors, or materials genes, which are engineered from primary features and a set …
In this article, we estimate structural labor supply with piecewise-linear budgets and nonseparable endogenous unobserved heterogeneity. We propose a two-stage method to address the endogeneity iss…
Transfer learning has attracted increasing attention in recent years for adaptively borrowing information across different data cohorts in various settings. Cancer registries have been widely used …
Momentum methods have been shown to accelerate the convergence of the standard gradient descent algorithm in practice and theory. In particular, the random partition based minibatch gradient descen…
An important problem in single-player video game design is how to sequence game elements within a level (or “chunk”) of the game. Each element has two critical features: a reward (e.g., earning…
We demonstrate a short-time long distance distributed high-temperature sensing by non-local Haar transform (NLH) in optical frequency domain reflectometry (OFDR). By searching similar pixels across…