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 36 篇，第 1 / 1 页

1. ICML 2026Accelerating Regression Tasks with Quantum Algorithms
2. ICML 2026Active Regression for Single-Index Models with Unknown Link Functions
3. ICML 2026Approximation Bounds for Transformer Networks with Application to Regression
4. ICML 2026AutoNumerics-Zero: Automated Discovery of State-of-the-Art Mathematical Functions
5. ICML 2026Beyond Accuracy and Complexity: The Effective Information Criterion for Structurally Stable Symbolic Regression
6. ICML 2026Beyond Token-level Supervision: Unlocking the Potential of Decoding-based Regression via Reinforcement Learning
7. ICML 2026Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression
8. ICML 2026Colorful Pinball: Density-Weighted Quantile Regression for Conditional Guarantee of Conformal Prediction
9. ICML 2026Contrastive Symbolic Regression: Aligned Representations, Adaptive Prediction, and Diverse Ensembles
10. ICML 2026Deep Neural Network Regression with Functional Covariates
11. ICML 2026Deep Single-Index Fréchet Regression
12. ICML 2026Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regression
13. ICML 2026Hard labels sampled from sparse targets mislead rotation invariant algorithms
14. ICML 2026Higher-Order Certified Robustness for Regression
15. ICML 2026How Hard Is Science?
16. ICML 2026Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback
17. ICML 2026Learning Randomized Reductions
18. ICML 2026Learning Treatment Representations for Downstream Instrumental Variable Regression
19. ICML 2026Must All Negatives Be Pushed Away Equally? Uncertainty-Aware Cross-View Geo-Localization via Normal Inverse Gamma Distribution
20. ICML 2026Neural–Evolutionary Symbolic Regression with Global Constraints: Constraint-Aware Decoding and Reward Shaping
21. ICML 2026Neuro-Symbolic AI for Analytical Solutions of Differential Equations
22. ICML 2026Nonparametric Distribution Regression Re-calibration
23. ICML 2026On Uniform Error Bounds for Kernel Regression under Non-Gaussian Noise
24. ICML 2026One-Shot Weighted Ensemble Estimation for Federated Quantile Regression: Optimal Statistical Guarantees under Heterogeneous Structured Data
25. ICML 2026Quantile-Free Uncertainty Quantification in Graph Neural Networks
26. ICML 2026RLIE: Rule Generation with Logistic Regression, Iterative Refinement, and Evaluation for Large Language Models
27. ICML 2026Scalable Simulation-Based Model Inference with Test-Time Complexity Control
28. ICML 2026Scaling Laws for Precision in High-Dimensional Linear Regression
29. ICML 2026Signal Strength Estimation in Logistic Regression Using Data Splitting
30. ICML 2026Spatial Conformal Inference through Localized Quantile Regression
31. ICML 2026Stabilizing Equation Learning via Zero-Point Constraints
32. ICML 2026Time-Series Decomposition as a Standalone Task: A Mechanism-Driven Diagnostic Benchmark
33. ICML 2026To Grok Grokking: Provable Grokking in Ridge Regression
34. ICML 2026Transformers Efficiently Perform In-Context Logistic Regression via Normalized Gradient Descent
35. ICML 2026TT-Sparse: Learning Sparse Rule Models with Differentiable Truth Tables
36. ICML 2026Variable Clustering via Distributionally Robust Nodewise Regression

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。