41 citations · 143 across the 39 of their papers we have counts for
5 papers · 1 filter
Inference with Mondrian Random Forests
Matias D. Cattaneo, Jason M. Klusowski, William G. Underwood
Random forests are popular methods for regression and classification analysis, and many different variants have been proposed in recent years. One interesting example is the Mondri…
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…
Error Reduction from Stacked Regressions
Xin Chen, Jason M. Klusowski, Yan Shuo Tan
Stacking regressions is an ensemble technique that forms linear combinations of different regression estimators to enhance predictive accuracy. The conventional approach uses cross…
On the Implicit Bias of Adam
Matias D. Cattaneo, Jason M. Klusowski, Boris Shigida
In previous literature, backward error analysis was used to find ordinary differential equations (ODEs) approximating the gradient descent trajectory. It was found that finite step…
Sharp Convergence Rates for Matching Pursuit
Jason M. Klusowski, Jonathan W. Siegel
We study the fundamental limits of matching pursuit, or the pure greedy algorithm, for approximating a target function by a linear combination of elements from a di…