1
Improved Distribution Estimation in $\ell_\infty$
无穷范数下分布估计新突破，理论保证更优，信息论与统计学习交汇。
arXiv:2605.30509v1 Announce Type: cross Abstract: We present improved bounds for estimating discrete probability distributions under the $\ell_\infty$…
2
Bayesian model selection and misspecification testing in imaging inverse problems only from noisy and partial measurements
贝叶斯方法实现成像逆问题的模型选择与错误指定测试，仅利用噪声和部分测量数据。
arXiv:2510.27663v3 Announce Type: replace-cross Abstract: Modern imaging techniques heavily rely on Bayesian statistical models to address difficult i…
3
Stabilizing distribution-free probabilistic forecasts
提出了一种稳定无分布概率预测的新方法，显著提升预测可靠性
arXiv:2605.28531v1 Announce Type: new Abstract: Multi-step-ahead forecasts are often updated as new observations become available, since shorter forec…
4
The Well-Tempered Classifier: Some Elementary Properties of Temperature Scaling
系统梳理温度缩放用于分类器校准的数学性质，揭示其理论基础与局限性，值得机器学习研究者细读。
arXiv:2602.14862v2 Announce Type: replace-cross Abstract: Temperature scaling is a simple method that allows to control the uncertainty of probabilist…
5
Minimax Limits of k-Fold Cross-Validation via Majority
探讨k折交叉验证通过多数投票法的最小最大极限，理论深度解析模型选择的新视角。
arXiv:2605.25859v1 Announce Type: cross Abstract: We study the mean-squared error of $k$-fold cross-validation as a risk estimator, with particular em…
6
Algometrics: Forecasting Under Algorithmic Feedback
预测算法反馈下的新型统计学方法，直面推荐系统与市场动态的因果难题。
arXiv:2605.23978v1 Announce Type: new Abstract: In algorithmic markets, predictive models become part of the data-generating process they aim to forec…
7
LLM Sparsity Prior for Robust Feature Selection
利用大模型蕴含的稀疏性先验，为高维数据特征选择提供鲁棒策略，理论贡献显著。
arXiv:2605.23102v1 Announce Type: cross Abstract: Large language models (LLMs) offer a scalable mechanism to elicit domain-informed prior information …
8
ASK HN: AI was always a probability problem?
AI的本质一直是概率问题？这篇讨论从序列模型到通用AI的模拟路径，引发对统计学习深层的反思。
AI was always a probability problem.. If we look at the emergence of sequence models (or statistical learning in broader sense), they predict the next…
9
Beyond Explained Variance: A Cautionary Tale of PCA
PCA中解释方差并非万能指标，本文通过实例警示其潜在陷阱，值得数据分析者关注。
arXiv:2605.13520v2 Announce Type: replace-cross Abstract: We address shortcomings of principal component analysis (PCA) for visualizing high-dimension…
10
Matrix-Decoupled Concentration for Autoregressive Sequences: Dimension-Free Guarantees for Sparse Long-Context Rewards
自回归序列的矩阵解耦集中不等式，为稀疏长上下文奖励提供无维度保证，理论创新突破。
arXiv:2605.06017v2 Announce Type: replace Abstract: Sequence-level evaluations in autoregressive Large Language Models (LLMs) rely on highly dependent…
11
Masking Causality and Conditional Dependence
探索因果关系与条件依赖的掩盖机制，为因果推断理论提供新见解
arXiv:2603.06984v2 Announce Type: replace-cross Abstract: Many regulatory and analytic problems require that a prohibited variable influence a decisio…
12
Density Estimation via Binless Multidimensional Integration
新方法BMTI通过无箱多维积分实现非参数密度估计，数据高效且鲁棒。
arXiv:2407.08094v3 Announce Type: replace-cross Abstract: We introduce the Binless Multidimensional Thermodynamic Integration (BMTI) method for nonpar…