Cover Image

Towards Scaling Laws for Symbolic Regression

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Otte, David, Franke, Jörg K. H., Zela, Arbër, Ferreira, Fábio, Hutter, Frank
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Can LLMs Beat Classical Hyperparameter Optimization Algorithms? A Study on autoresearch
by: Ferreira, Fabio, et al.
Published: (2026)

Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues
by: Grazzi, Riccardo, et al.
Published: (2024)

Multi-Objective Hierarchical Optimization with Large Language Models
by: Schwanke, Andrej, et al.
Published: (2026)

Improving LLM-based Global Optimization with Search Space Partitioning
by: Schwanke, Andrej, et al.
Published: (2025)

TempoPFN: Synthetic Pre-training of Linear RNNs for Zero-shot Time Series Forecasting
by: Moroshan, Vladyslav, et al.
Published: (2025)

HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models
by: Sukthanker, Rhea Sanjay, et al.
Published: (2024)

GAMformer: Bridging Tabular Foundation Models and Interpretable Machine Learning
by: Mueller, Andreas, et al.
Published: (2024)

Regularized Neural Ensemblers
by: Arango, Sebastian Pineda, et al.
Published: (2024)

Ensembling Finetuned Language Models for Text Classification
by: Arango, Sebastian Pineda, et al.
Published: (2024)

DeltaProduct: Improving State-Tracking in Linear RNNs via Householder Products
by: Siems, Julien, et al.
Published: (2025)

Multi-objective Differentiable Neural Architecture Search
by: Sukthanker, Rhea Sanjay, et al.
Published: (2024)

Balancing Synthetic Data and Replay for Enhancing Task-Specific Capabilities
by: Spiegelhalter, Urs, et al.
Published: (2025)

Transfer Learning for Finetuning Large Language Models
by: Strangmann, Tobias, et al.
Published: (2024)

Improving Deep Learning Optimization through Constrained Parameter Regularization
by: Franke, Jörg K. H., et al.
Published: (2023)

Rethinking Performance Measures of RNA Secondary Structure Problems
by: Runge, Frederic, et al.
Published: (2023)

Evolving Many Worlds: Towards Open-Ended Discovery in Petri Dish NCA via Population-Based Training
by: Berdica, Uljad, et al.
Published: (2026)

Increasing LLM Coding Capabilities through Diverse Synthetic Coding Tasks
by: Abed, Amal, et al.
Published: (2025)

Learning in Compact Spaces with Approximately Normalized Transformer
by: Franke, Jörg K. H., et al.
Published: (2025)

Fast Optimizer Benchmark
by: Blauth, Simon, et al.
Published: (2024)

Scaling Up Unbiased Search-based Symbolic Regression
by: Kahlmeyer, Paul, et al.
Published: (2025)

Towards Robust Scaling Laws for Optimizers
by: Volkova, Alexandra, et al.
Published: (2026)

Exhaustive Symbolic Regression
by: Bartlett, Deaglan J., et al.
Published: (2022)

Practitioner Motives to Use Different Hyperparameter Optimization Methods
by: Kannengießer, Niclas, et al.
Published: (2022)

Neural Scaling Laws for Deep Regression
by: Cadez, Tilen, et al.
Published: (2025)

Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How
by: Arango, Sebastian Pineda, et al.
Published: (2023)

One-shot World Models Using a Transformer Trained on a Synthetic Prior
by: Ferreira, Fabio, et al.
Published: (2024)

SPINEX_ Symbolic Regression: Similarity-based Symbolic Regression with Explainable Neighbors Exploration
by: Naser, MZ, et al.
Published: (2024)

Diffusion-Based Symbolic Regression
by: Bastiani, Zachary, et al.
Published: (2025)

Dimension Reduction for Symbolic Regression
by: Kahlmeyer, Paul, et al.
Published: (2025)

Decomposable Neuro Symbolic Regression
by: Morales, Giorgio, et al.
Published: (2025)

Fast Symbolic Regression Benchmarking
by: Martinek, Viktor
Published: (2025)

Beyond Random Augmentations: Pretraining with Hard Views
by: Ferreira, Fabio, et al.
Published: (2023)

Scaling Laws for Precision in High-Dimensional Linear Regression
by: Zhang, Dechen, et al.
Published: (2026)

Scaling Law for Stochastic Gradient Descent in Quadratically Parameterized Linear Regression
by: Ding, Shihong, et al.
Published: (2025)

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks
by: Lee, Dongwoo, et al.
Published: (2025)

Equality Graph Assisted Symbolic Regression
by: de Franca, Fabricio Olivetti, et al.
Published: (2025)

Discovering Symmetries of ODEs by Symbolic Regression
by: Kahlmeyer, Paul, et al.
Published: (2025)

Prior-Guided Symbolic Regression: Towards Scientific Consistency in Equation Discovery
by: Xiao, Jing, et al.
Published: (2026)

Bayesian Symbolic Regression for Missing Physics
by: Strouwen, Arno
Published: (2026)

Functional Scaling Laws in Kernel Regression: Loss Dynamics and Learning Rate Schedules
by: Li, Binghui, et al.
Published: (2025)