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Found 5 papers, 1 papers with code
TabGNN: Multiplex Graph Neural Network for Tabular Data Prediction
Tabular data prediction (TDP) is one of the most popular industrial applications, and various methods have been designed to improve the prediction performance.
Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction
To tackle the above challenges, we employ gradient boosting decision trees (GBDT) to handle data heterogeneity and introduce multi-task learning (MTL) to solve data insufficiency.
AutoSpeech 2020: The Second Automated Machine Learning Challenge for Speech Classification
The AutoSpeech challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to speech processing tasks.
Differential Private Stack Generalization with an Application to Diabetes Prediction
To meet the standard of differential privacy, noise is usually added into the original data, which inevitably deteriorates the predicting performance of subsequent learning algorithms.
Fast Learning with Nonconvex L1-2 Regularization
In this paper, we show that a closed-form solution can be derived for the proximal step associated with this regularizer.
