arXiv AI
Jun 9
FunctionEvolve: Structure-Guided Symbolic Regression with LLMs
arXiv:2606. 07704v1 Announce Type: cross Abstract: Symbolic regression aims to uncover explicit scientific laws from data.
Coefficient Calibration as Selection Pressure in Symbolic Regression
The paper introduces Dirichlet‑Sinkhorn Constant Averaging (DSCA), a calibration method for memetic symbolic regression that partitions data into subsets with varying covariate distributions, calibrates each candidate independently, and evaluates it using the mean of the resulting parameters. DSCA reduces the excess loss compared to centralized calibration for correctly specified models and improves functional recovery and the accuracy‑complexity trade‑off on synthetic and real datasets. The authors demonstrate that DSCA’s performance gains stem from a controlled heterogeneous calibration that adds a complementary source of selection pressure in symbolic‑regression search.
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arXiv AI
Jun 30
Evolutional Math: Cross-Validated Island-Model Genetic Programming for Interpretable Symbolic Regression on Small, Wide Datasets
arXiv:2606. 28381v1 Announce Type: cross Abstract: Symbolic regression via genetic programming routinely fails on small, wide datasets - a regime common in clinical-trial monitoring, biostatistics, and engineering pilot studies - by converging on bloated, overfit expressions that exploit correlation rather than prediction.
arXiv Machine Learning
Sep 22
Sparse Regression Distilled from a Single Robust Fit
arXiv:2609.23937v1 Announce Type: cross Abstract: Robust linear fits can resist response contamination yet remain too dense or unstable for useful global explanations. We propose penalized distillati...
arXiv AI
Jul 31
Shared Symbolic Backbones for Physically Consistent Multi-Output Symbolic Regression
arXiv:2607. 26528v1 Announce Type: cross Abstract: Symbolic regression provides analytical expressions, but it is usually applied one output at a time.
arXiv Machine Learning
Jun 9
Decomposable Neuro Symbolic Regression
arXiv:2511. 04124v3 Announce Type: replace Abstract: Symbolic regression (SR) models complex systems by discovering mathematical expressions that capture underlying relationships in observed data.
arXiv Statistics ML
Oct 15
Subdata Selection for Measurement-Constrained Linear Regression under Model Misspecification
arXiv:2610.19351v1 Announce Type: cross Abstract: In many modern applications, obtaining response variables is costly, time-consuming, or technically challenging, making it impractical to measure out...
arXiv AI
6d ago
Predicting Human-Gain Curves under Local Proxy Optimization from Repeated Ratings
arXiv:2610. 41114v1 Announce Type: new Abstract: Optimizing a proxy objective can improve human-perceived quality, but the resulting gain can vary with optimization strength.
arXiv Machine Learning
Jul 21
Isotonic Conformal Prediction
arXiv:2607. 16675v1 Announce Type: cross Abstract: A point prediction that is well calibrated on average can still be systematically biased conditional on its own value, undermining its use in downstream decision-making.
arXiv Machine Learning
Aug 11
Conformal Calibration for Multi-Modal Regression with Missing Modalities
arXiv:2608. 07795v1 Announce Type: cross Abstract: Prediction intervals for multi-modal regression with tabular variables, text, images, or other input sources are difficult to calibrate when those sources disagree or one is missing.
arXiv Machine Learning
Oct 28
DIGEST: Discovery-Guided LLM Tuning for Symbolic Regression
DIGEST is a new method for symbolic regression that combines a lightweight LLM with Genetic Programming. It uses a pretrained decoder block as a symbolic policy to seed GP populations, providing flexible generation of candidate expressions. The approach also feeds high‑quality GP discoveries back into the LLM to refine future generations, leading to improved recovery rates and faster search times on benchmark datasets.
arXiv Statistics ML
Oct 28
Multi-Variable Conformal Prediction: Optimizing Prediction Sets using Multiple Calibration Variables
The paper introduces Multi-Variable Conformal Prediction (MCP), a framework that jointly optimizes and calibrates multiple prediction‑set parameters in a single optimization problem, thereby eliminating the need for a calibration data split while preserving finite‑sample coverage guarantees. Two tractable variants are proposed: RemMCP, which uses constrained optimization with constraint removal and generalizes split conformal prediction, and RelMCP, which employs penalized optimization with constraint relaxation and can handle non‑convex problems. Experiments on ellipsoidal and multi‑modal prediction sets show that both RemMCP and an adaptive version of RelMCP consistently achieve target coverage with lower variance across calibration runs and, in most cases, produce smaller or comparable prediction sets compared to baseline methods.
arXiv Machine Learning
Jun 2
Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA
arXiv:2606. 00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems.