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

118 papers, page 2 of 3

51. NeurIPS 2025Neural Collapse in Cumulative Link Models for Ordinal Regression: An Analysis with Unconstrained Feature Model
52. NeurIPS 2025Nonparametric Quantile Regression with ReLU-Activated Recurrent Neural Networks
53. NeurIPS 2025Optimal kernel regression bounds under energy-bounded noise
54. NeurIPS 2025Optimal Rates in Continual Linear Regression via Increasing Regularization
55. NeurIPS 2025Personalized Decision Modeling: Utility Optimization or Textualized-Symbolic Reasoning
56. NeurIPS 2025Regularized least squares learning with heavy-tailed noise is minimax optimal
57. NeurIPS 2025Risk Bounds For Distributional Regression
58. NeurIPS 2025Robust Estimation Under Heterogeneous Corruption Rates
59. NeurIPS 2025Semi-Supervised Regression with Heteroscedastic Pseudo-Labels
60. NeurIPS 2025Statistical Inference for Gradient Boosting Regression
61. NeurIPS 2025SymMaP: Improving Computational Efficiency in Linear Solvers through Symbolic Preconditioning
62. NeurIPS 2025Transfer Learning for Benign Overfitting in High-Dimensional Linear Regression
63. NeurIPS 2025TranSUN: A Preemptive Paradigm to Eradicate Retransformation Bias Intrinsically from Regression Models in Recommender Systems
64. NeurIPS 2025Uncertainty Quantification for Deep Regression using Contextualised Normalizing Flows
65. NeurIPS 2024A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression
66. NeurIPS 2024A Walsh Hadamard Derived Linear Vector Symbolic Architecture
67. NeurIPS 2024Approximated Orthogonal Projection Unit: Stabilizing Regression Network Training Using Natural Gradient
68. NeurIPS 2024Attention boosted Individualized Regression
69. NeurIPS 2024Continuous Heatmap Regression for Pose Estimation via Implicit Neural Representation
70. NeurIPS 2024Data subsampling for Poisson regression with pth-root-link
71. NeurIPS 2024Distributed Sparse Regression via Penalization
72. NeurIPS 2024Estimating Generalization Performance Along the Trajectory of Proximal SGD in Robust Regression
73. NeurIPS 2024GraphTrail: Translating GNN Predictions into Human-Interpretable Logical Rules
74. NeurIPS 2024High-dimensional (Group) Adversarial Training in Linear Regression
75. NeurIPS 2024Implicit Regularization of Decentralized Gradient Descent for Sparse Regression
76. NeurIPS 2024Implicit Regularization Paths of Weighted Neural Representations
77. NeurIPS 2024Improving Adaptivity via Over-Parameterization in Sequence Models
78. NeurIPS 2024Least Squares Regression Can Exhibit Under-Parameterized Double Descent
79. NeurIPS 2024Linear Regression using Heterogeneous Data Batches
80. NeurIPS 2024Model Collapse Demystified: The Case of Regression
81. NeurIPS 2024Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning
82. NeurIPS 2024Nonparametric Instrumental Variable Regression through Stochastic Approximate Gradients
83. NeurIPS 2024Nonparametric Regression for 3D Point Cloud Learning
84. NeurIPS 2024Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms
85. NeurIPS 2024Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality
86. NeurIPS 2024Regression under demographic parity constraints via unlabeled post-processing
87. NeurIPS 2024Revisiting Differentially Private ReLU Regression
88. NeurIPS 2024Robust Sparse Regression with Non-Isotropic Designs
89. NeurIPS 2024Sample Selection via Contrastive Fragmentation for Noisy Label Regression
90. NeurIPS 2024Symbolic Regression with a Learned Concept Library
91. NeurIPS 2024Symmetry-Informed Governing Equation Discovery
92. NeurIPS 2024The Prevalence of Neural Collapse in Neural Multivariate Regression
93. NeurIPS 2024The Reliability of OKRidge Method in Solving Sparse Ridge Regression Problems
94. NeurIPS 2024The Space Complexity of Approximating Logistic Loss
95. NeurIPS 2024Towards the Dynamics of a DNN Learning Symbolic Interactions
96. NeurIPS 2024Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression
97. NeurIPS 2023Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression
98. NeurIPS 2023Collaboratively Learning Linear Models with Structured Missing Data
99. NeurIPS 2023D-CIPHER: Discovery of Closed-form Partial Differential Equations
100. NeurIPS 2023Demographic Parity Constrained Minimax Optimal Regression under Linear Model

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.