9 papers · 1 filter
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…
Statistical-Computational Trade-offs for Recursive Adaptive Partitioning Estimators
Yan Shuo Tan, Jason M. Klusowski, Krishnakumar Balasubramanian
Models based on recursive adaptive partitioning such as decision trees and their ensembles are popular for high-dimensional regression as they can potentially avoid the curse of di…
Robust Transfer Learning with Unreliable Source Data
Jianqing Fan, Cheng Gao, Jason M. Klusowski
This paper addresses challenges in robust transfer learning stemming from ambiguity in Bayes classifiers and weak transferable signals between the target and source distribution. W…