11 papers
Accuracy Limits of Causal Trees for Individualized Treatment Effects
Matias D. Cattaneo, Jason M. Klusowski, Ruiqi Rae Yu
Recursive decision trees are widely used to estimate heterogeneous causal treatment effects in experimental and observational studies. These methods are typically implemented using…
High-Dimensional Statistics: Reflections on Progress and Open Problems
Arian Maleki, Subhabrata Sen, Sivaraman Balakrishnan +9
Over the past two decades, the field of high-dimensional statistics has experienced substantial progress, driven largely by technological advances that have dramatically reduced th…
Coupled Training with Privileged Information and Unlabeled Data
Jiahao Shi, Omar Hagrass, Jason M. Klusowski
In many prediction problems, we have extra information during training (for example, measurements that are expensive or slow to collect) that will not be available when the model i…
Classification Imbalance as Transfer Learning
Eric Xia, Jason M. Klusowski
Classification imbalance arises when one class is much rarer than the other. We frame this setting as transfer learning under label (prior) shift between an imbalanced source distr…
Revisiting Randomization in Greedy Model Search
Xin Chen, Jason M. Klusowski, Yan Shuo Tan +1
Feature subsampling is a core component of random forests and other ensemble methods. While recent theory suggests that this randomization acts solely as a variance reduction mecha…
Stochastic Gradient Descent for Nonparametric Additive Regression
Xin Chen, Jason M. Klusowski
This paper introduces an iterative algorithm for training nonparametric additive models that enjoys favorable memory storage and computational requirements. The algorithm can be vi…