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May 8, 2026Scientia Sinica Mathematica0 citations

Algorithmic Inference: A Modern Machine Learning Approach by blending Algorithmic Thinking and Inferential Thinking

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ZZZhihua Zhang

Key Points

  • This research aims to explore how combining algorithmic thinking and inferential thinking enhances machine learning methods.
  • Examined the structure of problems and potential data distributions for algorithm design.
  • Studied high-dimensional statistical inference and adaptive algorithms from the perspectives of sample and computational efficiency.
  • Introduced a framework for algorithmic inference that produces logical and trustworthy conclusions from data.
  • Highlighting the progression and potential pathways for further advancements in algorithmic inference techniques.

Abstract

机器学习是探索基于数据的生成、预测和决策等任务的算法构造及其机理分析的学科,它试图通过经验和交互来提升机器或系统的智能,由此导致了现代人工智能技术的革命性突破。数学上,现代人工智能技术可以描述为如何求解具有组合结构的高维复杂问题。所以我们应该利用所关注问题的内在结构和数据的潜在分布信息来考虑算法设计和理论分析。通过结合随机算法和数据驱动两种思路来求解问题,从样本有效性和计算有效性两个维度来研究高维统计推断和自适应算法,这孕育了现代机器学习方法--算法推断。它利用算法从数据及其潜在分布推断出逻辑严谨、可证明、可信任的结论。本文将具体地回顾与展望算法推断的思想、进展和潜在方向。

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Cite This Study

Zhihua Zhang (2026) studied this question.

synapsesocial.com/papers/69fd7ddcbfa21ec5bbf060e5https://doi.org/10.1360/ssm-2026-0032
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