Symbolic Regression papers, 2023–2026
Equation discovery
Scope: NeurIPS, ICLR, ICML and CVPR, 2023–2026. 316 papers are tagged with this topic.
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.
Papers per venue
Counts by year and venue
| Year | NeurIPS | ICLR | ICML | CVPR | Total |
|---|---|---|---|---|---|
| 2023 | 22 | 17 | 21 | 3 | 63 |
| 2024 | 32 | 15 | 24 | 10 | 81 |
| 2025 | 30 | 22 | 21 | 2 | 75 |
| 2026 | 34 | 24 | 36 | 3 | 97 |
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
316 papers, page 5 of 7
- ICML 2024Beyond the Norms: Detecting Prediction Errors in Regression Models
- ICML 2024Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum
- ICML 2024Coresets for Multiple $\ell_p$ Regression
- ICML 2024Deep Regression Representation Learning with Topology
- ICML 2024Differentially Private Post-Processing for Fair Regression
- ICML 2024Federated Neuro-Symbolic Learning
- ICML 2024First-Order Manifold Data Augmentation for Regression Learning
- ICML 2024Generalization in Kernel Regression Under Realistic Assumptions
- ICML 2024Geometry-Aware Instrumental Variable Regression
- ICML 2024Inferring Change Points in High-Dimensional Linear Regression via Approximate Message Passing
- ICML 2024Neuro-Symbolic Temporal Point Processes
- ICML 2024No Double Descent in Principal Component Regression: A High-Dimensional Analysis
- ICML 2024Online Linear Regression in Dynamic Environments via Discounting
- ICML 2024Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
- ICML 2024PAGER: Accurate Failure Characterization in Deep Regression Models
- ICML 2024Pruner-Zero: Evolving Symbolic Pruning Metric From Scratch for Large Language Models
- ICML 2024Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise
- ICML 2024Simulation-Based Inference with Quantile Regression
- ICML 2024TIC-TAC: A Framework For Improved Covariance Estimation In Deep Heteroscedastic Regression
- ICML 2024Towards General Algorithm Discovery for Combinatorial Optimization: Learning Symbolic Branching Policy from Bipartite Graph
- ICML 2024Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression
- NeurIPS 2024A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression
- NeurIPS 2024A Walsh Hadamard Derived Linear Vector Symbolic Architecture
- NeurIPS 2024Approximated Orthogonal Projection Unit: Stabilizing Regression Network Training Using Natural Gradient
- NeurIPS 2024Attention boosted Individualized Regression
- NeurIPS 2024Continuous Heatmap Regression for Pose Estimation via Implicit Neural Representation
- NeurIPS 2024Data subsampling for Poisson regression with pth-root-link
- NeurIPS 2024Distributed Sparse Regression via Penalization
- NeurIPS 2024Estimating Generalization Performance Along the Trajectory of Proximal SGD in Robust Regression
- NeurIPS 2024GraphTrail: Translating GNN Predictions into Human-Interpretable Logical Rules
- NeurIPS 2024High-dimensional (Group) Adversarial Training in Linear Regression
- NeurIPS 2024Implicit Regularization of Decentralized Gradient Descent for Sparse Regression
- NeurIPS 2024Implicit Regularization Paths of Weighted Neural Representations
- NeurIPS 2024Improving Adaptivity via Over-Parameterization in Sequence Models
- NeurIPS 2024Least Squares Regression Can Exhibit Under-Parameterized Double Descent
- NeurIPS 2024Linear Regression using Heterogeneous Data Batches
- NeurIPS 2024Model Collapse Demystified: The Case of Regression
- NeurIPS 2024Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning
- NeurIPS 2024Nonparametric Instrumental Variable Regression through Stochastic Approximate Gradients
- NeurIPS 2024Nonparametric Regression for 3D Point Cloud Learning
- NeurIPS 2024Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms
- NeurIPS 2024Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality
- NeurIPS 2024Regression under demographic parity constraints via unlabeled post-processing
- NeurIPS 2024Revisiting Differentially Private ReLU Regression
- NeurIPS 2024Robust Sparse Regression with Non-Isotropic Designs
- NeurIPS 2024Sample Selection via Contrastive Fragmentation for Noisy Label Regression
- NeurIPS 2024Symbolic Regression with a Learned Concept Library
- NeurIPS 2024Symmetry-Informed Governing Equation Discovery
- NeurIPS 2024The Prevalence of Neural Collapse in Neural Multivariate Regression
- NeurIPS 2024The Reliability of OKRidge Method in Solving Sparse Ridge Regression Problems
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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.