Robuta

https://openreview.net/forum?id=0gvtoxhvMY¬eId=bBcG4XGOE8 Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition |... This paper introduces a new approach to address the issue of class imbalance in graph neural networks (GNNs) for learning on graph-structured data. Our... bias variancerethinkingsemisupervisedimbalanced https://openreview.net/forum?id=0gvtoxhvMY Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition |... This paper introduces a new approach to address the issue of class imbalance in graph neural networks (GNNs) for learning on graph-structured data. Our... bias variancerethinkingsemisupervisedimbalanced https://openreview.net/forum?id=nIF8XvtIr7 A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets | OpenReview Recent studies have highlighted the benefits of generating multiple synthetic datasets for supervised learning, from increased accuracy to more effective model... bias variance decomposition https://openreview.net/forum?id=i2Phucne30 On Bias-Variance Alignment in Deep Models | OpenReview Classical wisdom in machine learning holds that the generalization error can be decomposed into bias and variance, and these two terms exhibit a... bias variancein deepalignmentmodelsopenreview https://openreview.net/forum?id=B1guPVr2h4 A Modern Take on the Bias-Variance Tradeoff in Neural Networks | OpenReview We provide evidence against classical claims about the bias-variance tradeoff and propose a novel decomposition for variance. on the bias https://www.analyticsvidhya.com/blog/2021/06/how-to-get-the-most-out-of-bias-variance-tradeoff/ Bias-Variance Tradeoff | How to get most out of Bias-Variance Tradeoff Aug 25, 2025 - In this post, we will explain the bias-variance tradeoff in machine learning and how we can get the most out of it as a data scientist bias variance tradeoffhow to get https://deepai.org/publication/sequential-ensemble-learning-for-outlier-detection-a-bias-variance-perspective Sequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective | DeepAI Sep 18, 2016 - 09/18/16 - Ensemble methods for classification and clustering have been effectively used for decades, while ensemble learning for outlier det... ensemble learningoutlier detectionbias variancesequential https://deepai.org/publication/adaptive-importance-sampling-meets-mirror-descent-a-bias-variance-tradeoff Adaptive Importance Sampling meets Mirror Descent: a Bias-variance tradeoff | DeepAI Oct 29, 2021 - 10/29/21 - Adaptive importance sampling is a widely spread Monte Carlo technique that uses a re-weighting strategy to iteratively estimate th... bias variance tradeoffimportance samplingmirror descentadaptivemeets https://jmlr.org/papers/v22/20-1211.html What Causes the Test Error? Going Beyond Bias-Variance via ANOVA the testgoing beyondbias variancecauseserror https://openreview.net/forum?id=4TnFbv16hK Bias/Variance is not the same as Approximation/Estimation | OpenReview We study the relation between two classical results: the bias-variance decomposition, and the approximation-estimation decomposition. Both are important... not the samebias varianceapproximationestimationopenreview https://deepai.org/publication/bias-variance-decomposition-of-overparameterized-regression-with-random-linear-features Bias-variance decomposition of overparameterized regression with random linear features | DeepAI Mar 10, 2022 - 03/10/22 - In classical statistics, the bias-variance trade-off describes how varying a model's complexity (e.g., number of fit parameters) a... bias variance decomposition https://aclanthology.org/2023.acl-long.877/ Two-Stage Fine-Tuning for Improved Bias and Variance for Large Pretrained Language Models - ACL... Lijing Wang, Yingya Li, Timothy Miller, Steven Bethard, Guergana Savova. Proceedings of the 61st Annual Meeting of the Association for Computational... https://www.aanda.org/articles/aa/ref/2024/06/aa49148-24/aa49148-24.html Bias versus variance when fitting multi-species molecular lines with a non-LTE radiative transfer... https://pmc.ncbi.nlm.nih.gov/articles/PMC10404064/ Bias Amplification and Variance Inflation in Distributed Lag Models Using Low-Spatial-Resolution... Distributed lag models (DLMs) are often used to estimate lagged associations and identify critical exposure windows. In a simulation study of prenatal nitrogen... https://jmlr.org/papers/v24/21-1313.html Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and... monte carlo https://deepai.org/publication/double-trouble-in-double-descent-bias-and-variance-s-in-the-lazy-regime Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime | DeepAI Mar 2, 2020 - 03/02/20 - Deep neural networks can achieve remarkable generalization performances while interpolating the training data perfectly. Rather th... double trouble