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Truthful Deep Mechanism Design for Revenue-Maximization in Edge Computing with Budget Constraints
Published in IEEE Transactions on Vehicular Technology ( Volume: 71, Issue: 1), 2022
A new approach of collaborative task offloading in edge computing, called truthful deep mechanism, is proposed by leveraging a multi-task machine learning model, where inherently inter-connected collaborator selection and pricing policy determination are decided by designing two deep neural networks.
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PHATE Representations Can Effectively Capture Continuous Population Structure in Human Genomic Data
Published in ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling, GRaM Workshop @ ICML , 2024
A colab tutorial of visualizing and analyzing human genomic data.
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Enhancing Supervised Visualization through Autoencoder and Random Forest Proximities for Out-of-Sample Extension
Published in IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2024, 2024
An out-of-sample extension method for the random forest-based supervised dimensionality reduction method, RF-PHATE, combining information learned from the random forest model with the function-learning capabilities of autoencoders.
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