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Summary of Fine-grained Dynamic Framework For Bias-variance Joint Optimization on Data Missing Not at Random, by Mingming Ha et al.


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

by Mingming Ha, Xuewen Tao, Wenfang Lin, Qionxu Ma, Wujiang Xu, Linxun Chen

First submitted to arxiv on: 24 May 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Machine Learning (stat.ML)

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The paper addresses the issue of missing values in practical applications such as recommendation systems and display advertising. Existing estimators and regularizers attempt to achieve unbiased estimation, but these methods have unbounded variances when propensity scores tend to zero, compromising their stability and robustness. The authors theoretically reveal limitations of regularization techniques and demonstrate that unbiasedness leads to unbounded variance for general estimators. They then develop a dynamic learning framework that jointly optimizes bias and variance, adaptively selecting an appropriate estimator for each user-item pair according to a predefined objective function. This approach reduces and bounds the generalization bounds and variances of models with theoretical guarantees.
Low GrooveSquid.com (original content) Low Difficulty Summary
The paper helps solve a problem in machine learning where missing values can hurt prediction performance. It shows that some existing methods have limitations when dealing with these missing values. The authors create a new way to train models that balances two important things: making sure the model is accurate (bias) and reducing how much the model varies from one prediction to another (variance). This approach makes the model more stable and robust.

Keywords

» Artificial intelligence  » Generalization  » Machine learning  » Objective function  » Regularization