Symbolic Regression papers, 2023–2026

Key findings

• Symbolic Regression went from 63 papers in 2023 to 97 in 2026, roughly 1.5x in three years.
• NeurIPS publishes the most Symbolic Regression work: 118 papers from 2023 to 2026, 37% of the four-venue total.
• The fastest-growing overlap is with Theory · learning theory: papers tagged with both rose from 1 in 2023 to 10 in 2026.
• Among the 5 topics under Program Synthesis, it ranks #1 by 2026 paper count; the fastest grower in the group, Theorem Proving, is up 13x versus 1.5x here.

Representative papers

Spread across years and venues so every edition shows up

• 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

All Symbolic Regression papers

97 papers, page 2 of 2

51. ICML 2026One-Shot Weighted Ensemble Estimation for Federated Quantile Regression: Optimal Statistical Guarantees under Heterogeneous Structured Data
52. ICML 2026Quantile-Free Uncertainty Quantification in Graph Neural Networks
53. ICML 2026RLIE: Rule Generation with Logistic Regression, Iterative Refinement, and Evaluation for Large Language Models
54. ICML 2026Scalable Simulation-Based Model Inference with Test-Time Complexity Control
55. ICML 2026Scaling Laws for Precision in High-Dimensional Linear Regression
56. ICML 2026Signal Strength Estimation in Logistic Regression Using Data Splitting
57. ICML 2026Spatial Conformal Inference through Localized Quantile Regression
58. ICML 2026Stabilizing Equation Learning via Zero-Point Constraints
59. ICML 2026Time-Series Decomposition as a Standalone Task: A Mechanism-Driven Diagnostic Benchmark
60. ICML 2026To Grok Grokking: Provable Grokking in Ridge Regression
61. ICML 2026Transformers Efficiently Perform In-Context Logistic Regression via Normalized Gradient Descent
62. ICML 2026TT-Sparse: Learning Sparse Rule Models with Differentiable Truth Tables
63. ICML 2026Variable Clustering via Distributionally Robust Nodewise Regression
64. NeurIPS 2026$R^3$: 3D Reconstruction via Relative Regression
65. NeurIPS 2026Adversarially Attacking Symbolic Vocabulary Vulnerabilities In LLM Planners
66. NeurIPS 2026Bounds on Extrapolation across Phase Transitions with Generalized Regression
67. NeurIPS 2026Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentation
68. NeurIPS 2026Conformal Prediction for Distribution-to-Distribution Regression
69. NeurIPS 2026Continuous-depth Deep Gaussian Processes
70. NeurIPS 2026DASS: A Solver-Agnostic Dynamic Auxiliary Search Strategy for Symbolic Regression
71. NeurIPS 2026Data Diversity Drives the Emergence of Symbolic Mechanisms Supporting Abstract Reasoning
72. NeurIPS 2026Deep Heteroskedastic Regression: Post-Hoc Variance Estimation from Latent Representations
73. NeurIPS 2026EditFlowSR: Revisable Expression Generation for Symbolic Regression
74. NeurIPS 2026Efficient algorithms for linear regression with heteroskedastic errors
75. NeurIPS 2026End-to-End Verification of Neuro-symbolic Automata via Contrastive Logit-Gaps
76. NeurIPS 2026Geometric Analysis of Neural Regression Collapse via Intrinsic Dimension
77. NeurIPS 2026Geometry-Constrained Kolmogorov–Arnold Networks: Learning Edge Geometry via Banach Duality
78. NeurIPS 2026In-Context Benign Overfitting: A Feature-Selection Model in Linear Regression ICL
79. NeurIPS 2026Interpretable Relational Inference with LLM-Guided Symbolic Dynamics Modeling
80. NeurIPS 2026Just Ramp-Up: Debiasing Regression-based Estimator for A/B Tests under Network Interference
81. NeurIPS 2026Kernel Value Regression in Offline Reinforcement Learning
82. NeurIPS 2026Learning Optimal Transport Plans Via Autoregressive Token Regression
83. NeurIPS 2026Learning with Synthetic Data via SGD in High-Dimensional Linear Regression
84. NeurIPS 2026Multiple Descent of Generalization Curve for Optimally Regularized Ridge Regression
85. NeurIPS 2026NesyProAct: Proactive Neural-Symbolic Control for Web Agents
86. NeurIPS 2026Optimal Linear Regression Without a Variance
87. NeurIPS 2026Pooling Versus Ensembling for Ridge Regression Under Covariate Shift
88. NeurIPS 2026Position: The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models
89. NeurIPS 2026Regret-Optimal Wasserstein-Robust Regression
90. NeurIPS 2026RL-Guided Contraction of Symbolic Tensor Networks for Quantum Circuit Equivalence
91. NeurIPS 2026Sparse Attention as Compact Kernel Regression
92. NeurIPS 2026Sup-Norm Error under Proportional Asymptotics: Phase Transitions under Linear Regression
93. NeurIPS 2026Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology
94. NeurIPS 2026The Minimax Rate of Online Isotonic Regression on Product Orders
95. NeurIPS 2026Transfer Learning of Linear Regression with Multiple Pretrained Models: Benefiting from More Pretrained Models via Overparameterization Debiasing
96. NeurIPS 2026Umbilic Multinomial Logistic Regression
97. NeurIPS 2026Very Fast Bayesian Additive Regression Trees on GPU

FAQ

How many Symbolic Regression papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

316 in total: 63 in 2023, 81 in 2024, 75 in 2025, 97 in 2026.

How many Symbolic Regression papers are at NeurIPS 2026?

NeurIPS 2026 has 34 Symbolic Regression papers, versus 30 the year before.

How many Symbolic Regression papers are at ICLR 2026?

ICLR 2026 has 24 Symbolic Regression papers, versus 22 the year before.

How many Symbolic Regression papers are at ICML 2026?

ICML 2026 has 36 Symbolic Regression papers, versus 21 the year before.

How many Symbolic Regression papers are at CVPR 2026?

CVPR 2026 has 3 Symbolic Regression papers, versus 2 the year before.

Which conference publishes the most Symbolic Regression papers?

NeurIPS, with 118 papers from 2023 to 2026.