UvA-DARE (Digital Academic Repository) Mlwhatif: What If You Could Stop Re-Implementing Your Machine Learning Pipeline Analyses over and Over?

UvA-DARE (Digital Academic Repository) Mlwhatif: What If You Could Stop Re-Implementing Your Machine Learning Pipeline Analyses over and Over?

Evolving Executable Pipeline Programs for AutoML with Language Models

This work presents LACE, an AutoML framework that instead searches over complete executable pipeline programs: an evolutionary loop maintains a population of scikit-learn-compatible Python classes, and a large language model acts as the variation operator.

Improving Data Preparation for CSV Files with LLMs

This work shows how machine learning and GenAI can be used to assist with two specific tasks: First, when reading CSV files, it needs to be decided whether the first row is a header or not, and how machine learning and GenAI can be used to assist with two specific tasks.

How to Build Machine-Learning Models for Molecular Science: A Step-by-Step, Annotated Tutorial

This tutorial provides a comprehensive, end-to-end workflow from raw data to deployed models,icitly designed for environmental chemists with limited prior experience in ML modeling while also providing practical guidance for other users seeking to strengthen their modeling workflows.

When code does not run: reproducibility challenges in materials machine learning benchmarks

Reproducible benchmarks are essential for advancing data-driven materials research by standardizing datasets, prediction tasks, and evaluation protocols. In machine learning benchmarks, reproducibility is often assumed to follow from releasing source code and documented software dependencies. However, dependency specif...

Design Docs Are All You Need: An AI-native Machine-Learning Performance Tool

SMART is described, a rigorous symbolic performance-modeling library for ML systems whose main branch contains almost no code, and Regenerated implementations reproduce hand-audited reference models to round-off precision, suggesting that design docs can be the durable artifact for ML-systems co-design tools.