一份用信赖域框架重新审视自适应矩估计的数学研究，适合想深挖优化器收敛原理的读者。
arXiv:2608.04026v1 Announce Type: cross Abstract: In this paper, we develop a trust-region framework for understanding the behavior of adaptive moment…
将带噪梯度下降理想化为过阻尼朗之万动力学，量化模型参数落入危险区域的概率，为训练安全提供理论保证。
arXiv:2607.07538v1 Announce Type: new Abstract: Training a model with noisy gradient descent can be idealized as overdamped Langevin dynamics on the l…
用可列表解码编码推动Boosting理论，交叉编码与学习的新视角
arXiv:2607.05791v1 Announce Type: cross Abstract: Boosting is a fundamental technique for generically improving the accuracy of learning algorithms (S…
一项新研究揭示Transformer在训练中会收敛到不变的算法核心，为理解模型行为与电路机制提供新视角。
arXiv:2602.22600v2 Announce Type: replace-cross Abstract: Training selects for behavior, not circuitry: many weight configurations can implement the s…
生成式AI模型是否会概率性地“复制”训练数据？这篇论文从理论上剖析了模型记忆与版权边界。
Article URL: https://download.ssrn.com/2026/7/6/7067878.pdf?response-content-disposition=inline&X-Amz-Security-Token=IQoJb3JpZ2luX2VjELH%2F%2F%2F%…
从布朗运动路径提取签名特征，理论证明其具备全局通用逼近能力，为序列建模打开新思路。
arXiv:2512.16396v2 Announce Type: replace-cross Abstract: We establish $L^p$-universal approximation theorems for general path-dependent and non-antic…
颠覆知识蒸馏常规认知：预训练表征只有等价类意义，匹配坐标是伪命题
arXiv:2607.03572v1 Announce Type: cross Abstract: Knowledge distillation is usually framed as a choice of what to match in the teacher - its logits, h…
大模型先验如何提升程序搜索中的经验风险最小化？理论+方法前沿新作
arXiv:2510.14331v3 Announce Type: replace Abstract: We study program-learning methods that are efficient in both samples and computation. Classical le…
从理论层面剖析持续学习如何抵御数据投毒攻击，为鲁棒AI提供新视角。
arXiv:2606.29841v1 Announce Type: new Abstract: Continual learning (CL), where a model is trained on a sequence of data tasks, is increasingly being a…
探索测度近似的新结构化方法，为机器学习和概率论提供理论工具
arXiv:2310.09149v3 Announce Type: replace-cross Abstract: We study the approximation of probability measures in the Wasserstein-$p$ distance by struct…
提出“无窥视调优”方法，为大模型后训练提供可证明的泛化界限与鲁棒性保障。
arXiv:2507.01752v4 Announce Type: replace-cross Abstract: Gradient-based optimization is the workhorse of deep learning, offering efficient and scalab…
证明EML树具备通用逼近能力，为混合机器学习架构奠定理论基础。
arXiv:2606.23179v1 Announce Type: new Abstract: The recently introduced EML (Exp-Minus-Log) function acts as continuous analogue of NAND gates, provid…
将形式化验证与PAC-Bayesian理论结合，为过程奖励模型提供可证实的泛化边界，是AI安全领域的硬核进展。
arXiv:2606.20740v1 Announce Type: cross Abstract: Process Reward Models (PRMs) provide step-level verification for Large Language Model (LLM) reasonin…
从统计视角剖析训练与泛化机制，为机器学习理论提供严谨数学支撑。
arXiv:2606.20299v1 Announce Type: cross Abstract: Deep learning has managed to evade numerous intuitions from classical statistics to achieve unpreced…
如何高效搜索好的假设？这篇论文从理论上探讨假设空间搜索的时间下界与算法效率，对机器学习模型选择有启发。
arXiv:2509.03734v3 Announce Type: replace-cross Abstract: In the hypothesis selection problem, we are given sample and query access to finite set of c…
ICML 2026收录，提出一种预-hoc微调预测的风险分解框架，为模型微调前的风险量化提供理论新工具。
arXiv:2606.17649v1 Announce Type: cross Abstract: The high cost of fine-tuning LLMs poses a significant economic barrier; pre-hoc performance predicti…
探索在线战略分类中随机化算法的新进展，揭示其如何应对博弈分类场景
arXiv:2602.06257v2 Announce Type: replace Abstract: Online strategic classification studies settings in which agents strategically modify their featur…
提出结构化非参数变分推断新方法，有效建模依赖潜在变量，突破传统变分推断局限。
arXiv:2606.15458v1 Announce Type: cross Abstract: Variational inference (VI) is a core engine of modern AI, enabling scalable approximate Bayesian lea…
利用对称性隐私保护新范式，正交等变Transformer让大模型推理更安全
arXiv:2606.16461v1 Announce Type: new Abstract: Running large language models locally is often impractical, pushing inference on sensitive text to thi…
探究核赌博机问题的算法与极小极大复杂度，理论机器学习新进展
arXiv:2606.11171v1 Announce Type: new Abstract: Gaussian-process upper confidence bound (GP-UCB) and decision-estimation-coefficient (DEC) methods may…