Zobrazeno 1 - 10
of 69
pro vyhledávání: '"Klusowski, Jason M."'
Models based on recursive partitioning such as decision trees and their ensembles are popular for high-dimensional regression as they can potentially avoid the curse of dimensionality. Because empirical risk minimization (ERM) is computationally infe
Externí odkaz:
http://arxiv.org/abs/2411.04394
Decoding strategies play a pivotal role in text generation for modern language models, yet a puzzling gap divides theory and practice. Surprisingly, strategies that should intuitively be optimal, such as Maximum a Posteriori (MAP), often perform poor
Externí odkaz:
http://arxiv.org/abs/2410.03968
Autor:
Liang, Annie, Jemielita, Thomas, Liaw, Andy, Svetnik, Vladimir, Huang, Lingkang, Baumgartner, Richard, Klusowski, Jason M.
Variable importance plays a pivotal role in interpretable machine learning as it helps measure the impact of factors on the output of the prediction model. Model agnostic methods based on the generation of "null" features via permutation (or related
Externí odkaz:
http://arxiv.org/abs/2402.03447
Autor:
Chen, Xin, Klusowski, Jason M.
This paper introduces an iterative algorithm for training nonparametric additive models that enjoys favorable memory storage and computational requirements. The algorithm can be viewed as the functional counterpart of stochastic gradient descent, app
Externí odkaz:
http://arxiv.org/abs/2401.00691
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 Mondrian random forest, in which the underlying constituent trees are constr
Externí odkaz:
http://arxiv.org/abs/2310.09702
This paper addresses challenges in robust transfer learning stemming from ambiguity in Bayes classifiers and weak transferable signals between the target and source distribution. We introduce a novel quantity called the ''ambiguity level'' that measu
Externí odkaz:
http://arxiv.org/abs/2310.04606
Stacking regressions is an ensemble technique that forms linear combinations of different regression estimators to enhance predictive accuracy. The conventional approach uses cross-validation data to generate predictions from the constituent estimato
Externí odkaz:
http://arxiv.org/abs/2309.09880
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 sizes implicitly regularize solutions because terms appearing in the
Externí odkaz:
http://arxiv.org/abs/2309.00079
We study the fundamental limits of matching pursuit, or the pure greedy algorithm, for approximating a target function $ f $ by a linear combination $f_n$ of $n$ elements from a dictionary. When the target function is contained in the variation space
Externí odkaz:
http://arxiv.org/abs/2307.07679
Decision tree learning is increasingly being used for pointwise inference. Important applications include causal heterogenous treatment effects and dynamic policy decisions, as well as conditional quantile regression and design of experiments, where
Externí odkaz:
http://arxiv.org/abs/2211.10805