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

102 papers, page 2 of 3

51. ICML 2025LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models
52. ICML 2025Meta Optimality for Demographic Parity Constrained Regression via Post-Processing
53. ICML 2025On Volume Minimization in Conformal Regression
54. ICML 2025Pareto-Optimal Fronts for Benchmarking Symbolic Regression Algorithms
55. ICML 2025PRIME: Deep Imbalanced Regression with Proxies
56. ICML 2025Sharp Generalization for Nonparametric Regression by Over-Parameterized Neural Networks: A Distribution-Free Analysis in Spherical Covariate
57. ICML 2025Theoretical Limitations of Ensembles in the Age of Overparameterization
58. ICML 2024$H$-Consistency Guarantees for Regression
59. ICML 2024A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data
60. ICML 2024Agnostic Sample Compression Schemes for Regression
61. ICML 2024Beyond the Norms: Detecting Prediction Errors in Regression Models
62. ICML 2024Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum
63. ICML 2024Coresets for Multiple $\ell_p$ Regression
64. ICML 2024Deep Regression Representation Learning with Topology
65. ICML 2024Differentially Private Post-Processing for Fair Regression
66. ICML 2024Federated Neuro-Symbolic Learning
67. ICML 2024First-Order Manifold Data Augmentation for Regression Learning
68. ICML 2024Generalization in Kernel Regression Under Realistic Assumptions
69. ICML 2024Geometry-Aware Instrumental Variable Regression
70. ICML 2024Inferring Change Points in High-Dimensional Linear Regression via Approximate Message Passing
71. ICML 2024Neuro-Symbolic Temporal Point Processes
72. ICML 2024No Double Descent in Principal Component Regression: A High-Dimensional Analysis
73. ICML 2024Online Linear Regression in Dynamic Environments via Discounting
74. ICML 2024Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
75. ICML 2024PAGER: Accurate Failure Characterization in Deep Regression Models
76. ICML 2024Pruner-Zero: Evolving Symbolic Pruning Metric From Scratch for Large Language Models
77. ICML 2024Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise
78. ICML 2024Simulation-Based Inference with Quantile Regression
79. ICML 2024TIC-TAC: A Framework For Improved Covariance Estimation In Deep Heteroscedastic Regression
80. ICML 2024Towards General Algorithm Discovery for Combinatorial Optimization: Learning Symbolic Branching Policy from Bipartite Graph
81. ICML 2024Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression
82. ICML 2023A Nearly-Optimal Bound for Fast Regression with $\ell_\infty$ Guarantee
83. ICML 2023Controllable Neural Symbolic Regression
84. ICML 2023Data-Driven Subgroup Identification for Linear Regression
85. ICML 2023Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search
86. ICML 2023Deep Regression Unlearning
87. ICML 2023Differentially Private Distributed Bayesian Linear Regression with MCMC
88. ICML 2023Efficient List-Decodable Regression using Batches
89. ICML 2023Extrapolated Random Tree for Regression
90. ICML 2023Flexible Model Aggregation for Quantile Regression
91. ICML 2023Gradient Descent Converges Linearly for Logistic Regression on Separable Data
92. ICML 2023Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression
93. ICML 2023Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network
94. ICML 2023Multicalibration as Boosting for Regression
95. ICML 2023Nearly Optimal Algorithms with Sublinear Computational Complexity for Online Kernel Regression
96. ICML 2023On the Optimality of Misspecified Kernel Ridge Regression
97. ICML 2023Optimizing Hyperparameters with Conformal Quantile Regression
98. ICML 2023Regression with Label Permutation in Generalized Linear Model
99. ICML 2023Regression with Sensor Data Containing Incomplete Observations
100. ICML 2023Second-order regression models exhibit progressive sharpening to the edge of stability

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.