理论 · statistics论文趋势 2023–2026

主要结论

• 理论 · statistics论文从 2023 年的 16 篇增长到 2026 年的 57 篇，三年间扩大到约 3.6 倍。
• NeurIPS 是发理论 · statistics论文最多的会议，2023–2026 年共 58 篇，占四个会议总量的 42%。
• 交叉方向里涨得最快的是理论 · learning theory：同时属于两个主题的论文从 2023 年的 1 篇增至 2026 年的 28 篇。
• 在「理论与统计」的 5 个主题里，它的 2026 年论文数排第 5；同组增长最快的理论 · learning theory是 5.6 倍，理论 · statistics是 3.6 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• NeurIPS 2026Asymptotic Anytime-Valid Inference for U-statistics
• ICML 2026A Theory of How Pretraining Shapes Inductive Bias in Fine-Tuning
• ICLR 2026The Gaussian-Head OFL Family: One-Shot Federated Learning from Client Global Statistics
• NeurIPS 2025A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics
• ICML 2025Contextual Bandits for Unbounded Context Distributions
• ICLR 2025Improving Pretraining Data Using Perplexity Correlations
• CVPR 2025Dynamic Group Normalization: Spatio-Temporal Adaptation to Evolving Data Statistics
• NeurIPS 2024FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning
• ICML 2024Neural Networks Learn Statistics of Increasing Complexity
• ICLR 2024Kernelised Normalising Flows
• NeurIPS 2023Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics
• ICML 2023Fair Densities via Boosting the Sufficient Statistics of Exponential Families
• ICLR 2023Don’t fear the unlabelled: safe semi-supervised learning via debiasing
• NeurIPS 2026Computationally sufficient statistics for Ising models
• ICML 2026Adaptive Multiscale Binary Expansion Tests for Independence
• ICLR 2026Bayesian Influence Functions for Hessian-Free Data Attribution
• NeurIPS 2025Backward Conformal Prediction
• ICML 2025Eigen Analysis of Conjugate Kernel and Neural Tangent Kernel
• ICLR 2025From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation
• NeurIPS 2024Statistical Multicriteria Benchmarking via the GSD-Front

全部理论 · statistics论文

共 138 篇，第 1 / 3 页

1. ICLR 2026A Law of Data Reconstruction for Random Features (And Beyond)
2. ICLR 2026Bayesian Influence Functions for Hessian-Free Data Attribution
3. ICLR 2026Beyond Spectra: Eigenvector Overlaps in Loss Geometry
4. ICLR 2026CaRe-BN: Precise Moving Statistics for Stabilizing Spiking Neural Networks in Reinforcement Learning
5. ICLR 2026Gradient Descent Dynamics of Rank-One Matrix Denoising
6. ICLR 2026High-Dimensional Analysis of Single-Layer Attention for Sparse-Token Classification
7. ICLR 2026Improved high-dimensional estimation with Langevin dynamics and stochastic weight averaging
8. ICLR 2026Learning the Inverse Temperature of Ising Models under Hard Constraints using One Sample
9. ICLR 2026Mean Estimation from Coarse Data: Characterizations and Efficient Algorithms
10. ICLR 2026Memory-Statistics Tradeoff in Continual Learning with Structural Regularization
11. ICLR 2026Predicting Kernel Regression Learning Curves from Only Raw Data Statistics
12. ICLR 2026Price of Quality: Sufficient Conditions for Sparse Recovery using Mixed-Quality Data
13. ICLR 2026Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime
14. ICLR 2026Statistical Advantage of Softmax Attention: Insights from Single-Location Regression
15. ICLR 2026The Gaussian-Head OFL Family: One-Shot Federated Learning from Client Global Statistics
16. ICML 2026A Random Matrix Perspective on the Consistency of Diffusion Models
17. ICML 2026A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning
18. ICML 2026A Studentized Spherical Harmonics–Based Nonparametric Two-Sample Test for Compositional and Directional Data
19. ICML 2026A Theory of Contrastive Learning with Natural Images
20. ICML 2026A Theory of How Pretraining Shapes Inductive Bias in Fine-Tuning
21. ICML 2026A theory of learning data statistics in diffusion models, from easy to hard
22. ICML 2026Adaptive Multiscale Binary Expansion Tests for Independence
23. ICML 2026Approximating f -Divergences with Rank Statistics
24. ICML 2026Beyond First-order Asymptotics in Sequential Mean Testing
25. ICML 2026Characterization of Gaussian Universality Breakdown in High-Dimensional Empirical Risk Minimization
26. ICML 2026Combinatorial Sparse PCA Beyond the Spiked Identity Model
27. ICML 2026Deep networks learn to parse uniform-depth context-free languages from local statistics
28. ICML 2026Deep Neural Network Regression with Functional Covariates
29. ICML 2026Deriving Neural Scaling Laws from the Statistics of Natural Language
30. ICML 2026Finite-Width Neural Tangent Kernels from Feynman Diagrams
31. ICML 2026Learning $U$-Statistics with Active Inference
32. ICML 2026Learning Gaussian Graphical Models from a Glauber Trajectory Without Mixing
33. ICML 2026Linear Regression with Unknown Truncation Beyond Gaussian Features
34. ICML 2026Mean-Shift PCA by Knockoff Mean
35. ICML 2026On the Existence of Consistent Adversarial Attacks in High-Dimensional Linear Classification
36. ICML 2026On the Power of Statistics in Class-Incremental Learning with Pretrained Models
37. ICML 2026Optimal Attention Temperature Improves the Robustness of In-Context Learning under Distribution Shift in High Dimensions
38. ICML 2026Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression
39. ICML 2026Provable Bounds for the Learnability of Sample-Compressible Families from Noisy Samples
40. ICML 2026Sample Complexity Bounds for Robust Mean Estimation with Mean-Shift Contamination
41. ICML 2026Sharp Concentration Bounds for Bundle-Valued Statistics on Manifolds
42. ICML 2026Sharp description of local minima in the loss landscape of high-dimensional two-layer ReLU neural networks
43. ICML 2026Symmetries in language statistics shape the geometry of model representations
44. ICML 2026The Catastrophic Failure of *the* k-Means Algorithm in High Dimensions, and How Hartigan's Algorithm Avoids It
45. ICML 2026When Softmax Fails at the Top: Extreme‑Value Corrections for InfoNCE
46. NeurIPS 2026Asymptotic Anytime-Valid Inference for U-statistics
47. NeurIPS 2026Computationally sufficient statistics for Ising models
48. NeurIPS 2026Hierarchical Concept Geometry in Language Representations Emerges from Word Co-occurrence
49. NeurIPS 2026Neural population tuning statistics as priors for multitask generalization
50. NeurIPS 2026Pretraining Data Statistics Shape the Phases of Learning Entity Comparison in Language Models

常见问题

2023–2026 年四大会议一共有多少篇理论 · statistics论文？

共 138 篇，按年份分别是 2023 年 16 篇、2024 年 21 篇、2025 年 44 篇、2026 年 57 篇。

NeurIPS 2026 有多少篇理论 · statistics论文？

NeurIPS 2026 有 12 篇理论 · statistics论文，上一届是 23 篇。

ICLR 2026 有多少篇理论 · statistics论文？

ICLR 2026 有 15 篇理论 · statistics论文，上一届是 7 篇。

ICML 2026 有多少篇理论 · statistics论文？

ICML 2026 有 30 篇理论 · statistics论文，上一届是 13 篇。

CVPR 2025 有多少篇理论 · statistics论文？

CVPR 2025 有 1 篇理论 · statistics论文。

哪个会议的理论 · statistics论文最多？

NeurIPS，2023–2026 年共 58 篇。