符号回归论文趋势 2023–2026

主要结论

• 符号回归论文从 2023 年的 63 篇增长到 2026 年的 97 篇，三年间扩大到约 1.5 倍。
• NeurIPS 是发符号回归论文最多的会议，2023–2026 年共 118 篇，占四个会议总量的 37%。
• 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 10 篇。
• 在「程序综合」的 5 个主题里，它的 2026 年论文数排第 1；同组增长最快的定理证明是 13 倍，符号回归是 1.5 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 2026Conformal Prediction for Distribution-to-Distribution Regression
• ICML 2026Approximation Bounds for Transformer Networks with Application to Regression
• ICLR 2026Fast Estimation of Wasserstein Distances via Regression on Sliced Wasserstein Distances
• CVPR 2026CoLoR The Devil is in Scene Coordinate Regression for Large-Scale
• NeurIPS 2025Ascent Fails to Forget
• ICML 2025Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression
• ICLR 2025A Graph Enhanced Symbolic Discovery Framework For Efficient Logic Optimization
• CVPR 2025R-SCoRe: Revisiting Scene Coordinate Regression for Robust Large-Scale Visual Localization
• NeurIPS 2024Distributed Sparse Regression via Penalization
• ICML 2024$H$-Consistency Guarantees for Regression
• ICLR 2024Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Tasks
• CVPR 2024Map-Relative Pose Regression for Visual Re-Localization
• NeurIPS 2023Collaboratively Learning Linear Models with Structured Missing Data
• ICML 2023A Nearly-Optimal Bound for Fast Regression with $\ell_\infty$ Guarantee
• ICLR 2023Fast Nonlinear Vector Quantile Regression
• CVPR 2023DistilPose: Tokenized Pose Regression With Heatmap Distillation
• NeurIPS 2026DASS: A Solver-Agnostic Dynamic Auxiliary Search Strategy for Symbolic Regression
• ICML 2026AutoNumerics-Zero: Automated Discovery of State-of-the-Art Mathematical Functions
• ICLR 2026GenSR: Symbolic regression based on equation generative space
• CVPR 2026Sparse-View Localization via Online Neural 3D Regression

全部符号回归论文

共 30 篇，第 1 / 1 页

1. NeurIPS 2025A Closer Look at NTK Alignment: Linking Phase Transitions in Deep Image Regression
2. NeurIPS 2025A learnability analysis on neuro-symbolic learning
3. NeurIPS 2025Adversarial Robustness of Nonparametric Regression
4. NeurIPS 2025Ascent Fails to Forget
5. NeurIPS 2025Compact Memory for Continual Logistic Regression
6. NeurIPS 2025Computational Efficiency under Covariate Shift in Kernel Ridge Regression
7. NeurIPS 2025Discovering Symbolic Partial Differential Equation by Abductive Learning
8. NeurIPS 2025Enhancing Deep Batch Active Learning for Regression with Imperfect Data Guided Selection
9. NeurIPS 2025Epistemic Uncertainty Estimation in Regression Ensemble Models with Pairwise Epistemic Estimators
10. NeurIPS 2025FAME: Adaptive Functional Attention with Expert Routing for Function-on-Function Regression
11. NeurIPS 2025FIGRDock: Fast Interaction-Guided Regression for Flexible Docking
12. NeurIPS 2025Generalization vs Specialization under Concept Shift
13. NeurIPS 2025Graph-based Symbolic Regression with Invariance and Constraint Encoding
14. NeurIPS 2025Improving Monte Carlo Tree Search for Symbolic Regression
15. NeurIPS 2025Information-Computation Tradeoffs for Noiseless Linear Regression with Oblivious Contamination
16. NeurIPS 2025Large Stepsizes Accelerate Gradient Descent for Regularized Logistic Regression
17. NeurIPS 2025Neural Collapse in Cumulative Link Models for Ordinal Regression: An Analysis with Unconstrained Feature Model
18. NeurIPS 2025Nonparametric Quantile Regression with ReLU-Activated Recurrent Neural Networks
19. NeurIPS 2025Optimal kernel regression bounds under energy-bounded noise
20. NeurIPS 2025Optimal Rates in Continual Linear Regression via Increasing Regularization
21. NeurIPS 2025Personalized Decision Modeling: Utility Optimization or Textualized-Symbolic Reasoning
22. NeurIPS 2025Regularized least squares learning with heavy-tailed noise is minimax optimal
23. NeurIPS 2025Risk Bounds For Distributional Regression
24. NeurIPS 2025Robust Estimation Under Heterogeneous Corruption Rates
25. NeurIPS 2025Semi-Supervised Regression with Heteroscedastic Pseudo-Labels
26. NeurIPS 2025Statistical Inference for Gradient Boosting Regression
27. NeurIPS 2025SymMaP: Improving Computational Efficiency in Linear Solvers through Symbolic Preconditioning
28. NeurIPS 2025Transfer Learning for Benign Overfitting in High-Dimensional Linear Regression
29. NeurIPS 2025TranSUN: A Preemptive Paradigm to Eradicate Retransformation Bias Intrinsically from Regression Models in Recommender Systems
30. NeurIPS 2025Uncertainty Quantification for Deep Regression using Contextualised Normalizing Flows

常见问题

2023–2026 年四大会议一共有多少篇符号回归论文？

共 316 篇，按年份分别是 2023 年 63 篇、2024 年 81 篇、2025 年 75 篇、2026 年 97 篇。

NeurIPS 2026 有多少篇符号回归论文？

NeurIPS 2026 有 34 篇符号回归论文，上一届是 30 篇。

ICLR 2026 有多少篇符号回归论文？

ICLR 2026 有 24 篇符号回归论文，上一届是 22 篇。

ICML 2026 有多少篇符号回归论文？

ICML 2026 有 36 篇符号回归论文，上一届是 21 篇。

CVPR 2026 有多少篇符号回归论文？

CVPR 2026 有 3 篇符号回归论文，上一届是 2 篇。

哪个会议的符号回归论文最多？

NeurIPS，2023–2026 年共 118 篇。