regAL: Python Package for Active Learning of Regression Problems
Fuente:
arXiv
Saved in:
| Main Authors: | Surzhikova, Elizaveta, Proppe, Jonny |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Analysing Python Machine Learning Notebooks with Moose
by: Mignard, Marius, et al.
Published: (2025)
by: Mignard, Marius, et al.
Published: (2025)
evomap: A Toolbox for Dynamic Mapping in Python
by: Matthe, Maximilian
Published: (2025)
by: Matthe, Maximilian
Published: (2025)
PyPulse: A Python Library for Biosignal Imputation
by: Gao, Kevin, et al.
Published: (2024)
by: Gao, Kevin, et al.
Published: (2024)
MLXP: A Framework for Conducting Replicable Experiments in Python
by: Arbel, Michael, et al.
Published: (2024)
by: Arbel, Michael, et al.
Published: (2024)
Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Python
by: Adamczyk, Jakub, et al.
Published: (2024)
by: Adamczyk, Jakub, et al.
Published: (2024)
Bridging the Language Gap: An Empirical Study of Bindings for Open Source Machine Learning Libraries Across Software Package Ecosystems
by: Li, Hao, et al.
Published: (2022)
by: Li, Hao, et al.
Published: (2022)
DocuMint: Docstring Generation for Python using Small Language Models
by: Poudel, Bibek, et al.
Published: (2024)
by: Poudel, Bibek, et al.
Published: (2024)
DomainLab: A modular Python package for domain generalization in deep learning
by: Sun, Xudong, et al.
Published: (2024)
by: Sun, Xudong, et al.
Published: (2024)
Can LLMs Find Bugs in Code? An Evaluation from Beginner Errors to Security Vulnerabilities in Python and C++
by: Mhatre, Akshay, et al.
Published: (2025)
by: Mhatre, Akshay, et al.
Published: (2025)
Practical programming research of Linear DML model based on the simplest Python code: From the standpoint of novice researchers
by: Yao, Shunxin
Published: (2025)
by: Yao, Shunxin
Published: (2025)
Towards a Neural Debugger for Python
by: Beck, Maximilian, et al.
Published: (2026)
by: Beck, Maximilian, et al.
Published: (2026)
Learning to Triage Taint Flows Reported by Dynamic Program Analysis in Node.js Packages
by: Ni, Ronghao, et al.
Published: (2025)
by: Ni, Ronghao, et al.
Published: (2025)
Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM
by: Ryan, Gabriel, et al.
Published: (2024)
by: Ryan, Gabriel, et al.
Published: (2024)
Risk-Aware Batch Testing for Performance Regression Detection
by: Sayedsalehi, Ali, et al.
Published: (2026)
by: Sayedsalehi, Ali, et al.
Published: (2026)
An Empirical Evaluation of Locally Deployed LLMs for Bug Detection in Python Code
by: Vulićević, Jelena Ilić
Published: (2026)
by: Vulićević, Jelena Ilić
Published: (2026)
A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent
by: Yang, James, et al.
Published: (2024)
by: Yang, James, et al.
Published: (2024)
iML: Executable, Problem-Grounded, and Broadly Exploratory Code-Driven AutoML
by: Le, Dat, et al.
Published: (2026)
by: Le, Dat, et al.
Published: (2026)
scicode-lint: Detecting Methodology Bugs in Scientific Python Code with LLM-Generated Patterns
by: Samsonau, Sergey V.
Published: (2026)
by: Samsonau, Sergey V.
Published: (2026)
Who Wins the Race? (R Vs Python) - An Exploratory Study on Energy Consumption of Machine Learning Algorithms
by: Chattaraj, Rajrupa, et al.
Published: (2025)
by: Chattaraj, Rajrupa, et al.
Published: (2025)
An Empirical Study on Package-Level Deprecation in Python Ecosystem
by: Zhong, Zhiqing, et al.
Published: (2024)
by: Zhong, Zhiqing, et al.
Published: (2024)
OpenClassGen: A Large-Scale Corpus of Real-World Python Classes for LLM Research
by: Rahman, Musfiqur, et al.
Published: (2025)
by: Rahman, Musfiqur, et al.
Published: (2025)
We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs
by: Spracklen, Joseph, et al.
Published: (2024)
by: Spracklen, Joseph, et al.
Published: (2024)
evclust: Python library for evidential clustering
by: Soubeiga, Armel, et al.
Published: (2025)
by: Soubeiga, Armel, et al.
Published: (2025)
Automated Reproducibility Has a Problem Statement Problem
by: Snelleman, Thijs, et al.
Published: (2025)
by: Snelleman, Thijs, et al.
Published: (2025)
Deep Learning and Machine Learning: Advancing Big Data Analytics and Management with Design Patterns
by: Chen, Keyu, et al.
Published: (2024)
by: Chen, Keyu, et al.
Published: (2024)
Learning-Based Testing for Deep Learning: Enhancing Model Robustness with Adversarial Input Prioritization
by: Rahman, Sheikh Md Mushfiqur, et al.
Published: (2025)
by: Rahman, Sheikh Md Mushfiqur, et al.
Published: (2025)
Assessing Large Language Models for Automated Feedback Generation in Learning Programming Problem Solving
by: Silva, Priscylla, et al.
Published: (2025)
by: Silva, Priscylla, et al.
Published: (2025)
Analysing the Behaviour of Tree-Based Neural Networks in Regression Tasks
by: Samoaa, Peter, et al.
Published: (2024)
by: Samoaa, Peter, et al.
Published: (2024)
Geospatial Machine Learning Libraries
by: Stewart, Adam J., et al.
Published: (2025)
by: Stewart, Adam J., et al.
Published: (2025)
Data Virtualization for Machine Learning
by: Khan, Saiful, et al.
Published: (2025)
by: Khan, Saiful, et al.
Published: (2025)
Robust Learning of Diverse Code Edits
by: Aggarwal, Tushar, et al.
Published: (2025)
by: Aggarwal, Tushar, et al.
Published: (2025)
Machine Learning Systems are Bloated and Vulnerable
by: Zhang, Huaifeng, et al.
Published: (2022)
by: Zhang, Huaifeng, et al.
Published: (2022)
Should Code Models Learn Pedagogically? A Preliminary Evaluation of Curriculum Learning for Real-World Software Engineering Tasks
by: Khant, Kyi Shin, et al.
Published: (2025)
by: Khant, Kyi Shin, et al.
Published: (2025)
A Regression Framework for Understanding Prompt Component Impact on LLM Performance
by: Lauziere, Andrew, et al.
Published: (2026)
by: Lauziere, Andrew, et al.
Published: (2026)
Targeted Deep Learning System Boundary Testing
by: Weißl, Oliver, et al.
Published: (2024)
by: Weißl, Oliver, et al.
Published: (2024)
Automated Trustworthiness Testing for Machine Learning Classifiers
by: Cho, Steven, et al.
Published: (2024)
by: Cho, Steven, et al.
Published: (2024)
How do Machine Learning Models Change?
by: Castaño, Joel, et al.
Published: (2024)
by: Castaño, Joel, et al.
Published: (2024)
Machine Learning Operations: A Mapping Study
by: Chakraborty, Abhijit, et al.
Published: (2024)
by: Chakraborty, Abhijit, et al.
Published: (2024)
Learning test generators for cyber-physical systems
by: Peltomäki, Jarkko, et al.
Published: (2024)
by: Peltomäki, Jarkko, et al.
Published: (2024)
SmartChoices: Augmenting Software with Learned Implementations
by: Golovin, Daniel, et al.
Published: (2023)
by: Golovin, Daniel, et al.
Published: (2023)
Similar Items
-
Analysing Python Machine Learning Notebooks with Moose
by: Mignard, Marius, et al.
Published: (2025) -
evomap: A Toolbox for Dynamic Mapping in Python
by: Matthe, Maximilian
Published: (2025) -
PyPulse: A Python Library for Biosignal Imputation
by: Gao, Kevin, et al.
Published: (2024) -
MLXP: A Framework for Conducting Replicable Experiments in Python
by: Arbel, Michael, et al.
Published: (2024) -
Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Python
by: Adamczyk, Jakub, et al.
Published: (2024)