Image de couverture de livre

Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Ha, Mingming, Tao, Xuewen, Lin, Wenfang, Ma, Qionxu, Xu, Wujiang, Chen, Linxun
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach
par: Xu, Wujiang, et autres
Publié: (2023)

Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis
par: Li, Yufan, et autres
Publié: (2024)

Deep Generative Imputation Model for Missing Not At Random Data
par: Chen, Jialei, et autres
Publié: (2023)

The Bias-Variance Tradeoff in Data-Driven Optimization: A Local Misspecification Perspective
par: Lan, Haixiang, et autres
Publié: (2025)

Randomization Can Reduce Both Bias and Variance: A Case Study in Random Forests
par: Liu, Brian, et autres
Publié: (2024)

SLMRec: Distilling Large Language Models into Small for Sequential Recommendation
par: Xu, Wujiang, et autres
Publié: (2024)

Modeling Information Blackouts in Missing Not-At-Random Time Series Data
par: Sunesh, Aman, et autres
Publié: (2026)

Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions
par: Xu, Wujiang, et autres
Publié: (2023)

Increasing Missingness to Reduce Bias: Richardson-SGD with Missing Data
par: Genans, Ferdinand, et autres
Publié: (2026)

On the Emergence of Weak-to-Strong Generalization: A Bias-Variance Perspective
par: Xu, Gengze, et autres
Publié: (2025)

A Unified Framework for Variable Selection in Model-Based Clustering with Missing Not at Random
par: Ho, Binh H., et autres
Publié: (2025)

Adaptive Optimization for Prediction with Missing Data
par: Bertsimas, Dimitris, et autres
Publié: (2024)

Understanding the Generalization of Bilevel Programming in Hyperparameter Optimization: A Tale of Bias-Variance Decomposition
par: Zhou, Yubo, et autres
Publié: (2026)

Joint Analysis of Single-Cell Data across Cohorts with Missing Modalities
par: Arriola, Marianne, et autres
Publié: (2024)

Extending Model-x Framework to Missing Data
par: Koyuncu, Deniz, et autres
Publié: (2022)

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution
par: Tu, Yiwen, et autres
Publié: (2025)

A Generalized Bias-Variance Decomposition for Bregman Divergences
par: Pfau, David
Publié: (2025)

Ratio-Variance Regularized Policy Optimization for Efficient LLM Fine-tuning
par: Luo, Yu, et autres
Publié: (2026)

Variance-Reducing Couplings for Random Features
par: Reid, Isaac, et autres
Publié: (2024)

Missing Pattern Recognized Diffusion Imputation Model for Missing Not At Random
par: Sim, Gyuwon, et autres
Publié: (2026)

Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models
par: Gautam, Tanmay, et autres
Publié: (2024)

Doubly Calibrated Estimator for Recommendation on Data Missing Not At Random
par: Kweon, Wonbin, et autres
Publié: (2024)

Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition
par: Yan, Liang, et autres
Publié: (2023)

On the Convergence of Experience Replay in Policy Optimization: Characterizing Bias, Variance, and Finite-Time Convergence
par: Zheng, Hua, et autres
Publié: (2021)

From Noisy Traces to Stable Gradients: Bias-Variance Optimized Preference Optimization for Aligning Large Reasoning Models
par: Zhu, Mingkang, et autres
Publié: (2025)

The Decaying Missing-at-Random Framework: Model Doubly Robust Causal Inference with Partially Labeled Data
par: Zhang, Yuqian, et autres
Publié: (2023)

Data Selection for LLM Alignment Using Fine-Grained Preferences
par: Zhang, Jia, et autres
Publié: (2025)

A Random Matrix Perspective of Echo State Networks: From Precise Bias--Variance Characterization to Optimal Regularization
par: Moakher, Yessin, et autres
Publié: (2025)

Recursive Equations For Imputation Of Missing Not At Random Data With Sparse Pattern Support
par: Phung, Trung, et autres
Publié: (2025)

Variance-Aware Linear UCB with Deep Representation for Neural Contextual Bandits
par: Bui, Ha Manh, et autres
Publié: (2024)

A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets
par: Räisä, Ossi, et autres
Publié: (2024)

A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models
par: Gruber, Sebastian G., et autres
Publié: (2023)

LumiGen: An LVLM-Enhanced Iterative Framework for Fine-Grained Text-to-Image Generation
par: Dong, Xiaoqi, et autres
Publié: (2025)

Graph4MM: Weaving Multimodal Learning with Structural Information
par: Ning, Xuying, et autres
Publié: (2025)

From Incomplete Coarse-Grained to Complete Fine-Grained: A Two-Stage Framework for Spatiotemporal Data Reconstruction
par: Sun, Ziyu, et autres
Publié: (2024)

How Ensemble Learning Balances Accuracy and Overfitting: A Bias-Variance Perspective on Tabular Data
par: Mohammad, Zubair Ahmed
Publié: (2025)

Variance-Gated Ensembles: An Epistemic-Aware Framework for Uncertainty Estimation
par: Gillis, H. Martin, et autres
Publié: (2026)

The Lipschitz-Variance-Margin Tradeoff for Enhanced Randomized Smoothing
par: Delattre, Blaise, et autres
Publié: (2023)

An Optimization Framework for Differentially Private Sparse Fine-Tuning
par: Makni, Mehdi, et autres
Publié: (2025)

Integrating Large Language Models with Graphical Session-Based Recommendation
par: Guo, Naicheng, et autres
Publié: (2024)