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Found 13 papers, 3 papers with code
AI for Low-Code for AI
We task 20 developers with varying levels of AI expertise with implementing four ML pipelines using LowCoder, replacing the LowCoder_NL component with a simple keyword search in half the tasks.
MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning
Training deep networks and tuning hyperparameters on large datasets is computationally intensive.
Navigating Ensemble Configurations for Algorithmic Fairness
Bias mitigators can improve algorithmic fairness in machine learning models, but their effect on fairness is often not stable across data splits.
Exploring Code Style Transfer with Neural Networks
In addition to defining style, we explore the capability of a pre-trained code language model to capture information about code style.
An Empirical Study of Modular Bias Mitigators and Ensembles
A popular approach to train more stable models is ensemble learning.
Pipeline Combinators for Gradual AutoML
Automated machine learning (AutoML) can make data scientists more productive.
RASL: Relational Algebra in Scikit-Learn Pipelines
Integrating data preparation with machine-learning (ML) pipelines has been a long- standing challenge.
Thinking Fast and Slow in AI
This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making.
Lale: Consistent Automated Machine Learning
Automated machine learning makes it easier for data scientists to develop pipelines by searching over possible choices for hyperparameters, algorithms, and even pipeline topologies.
Mining Documentation to Extract Hyperparameter Schemas
Our vision is to reduce the burden to manually create and maintain such schemas for AI automation tools and broaden the reach of automation to larger libraries and richer schemas.
Type-Driven Automated Learning with Lale
Machine-learning automation tools, ranging from humble grid-search to hyperopt, auto-sklearn, and TPOT, help explore large search spaces of possible pipelines.
A semi-supervised deep learning algorithm for abnormal EEG identification
Systems that can automatically analyze EEG signals can aid neurologists by reducing heavy workload and delays.
Yaps: Python Frontend to Stan
Stan is a popular probabilistic programming language with a self-contained syntax and semantics that is close to graphical models.
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