Machine Learning

DiRP Trustworthy LLM

Directed Reading Program (DiRP) on trustworthy large language models.

Holistic Trustworthy ML

Instead of isolated properties, we target on a holistic trustworthiness covering every properties in one solution.

Federated Learning

On the need of data privacy and more data, we strive to join the knowledge from a fair amount of users to train powerful deep neural networks without sharing data.

Evaluate Binary Classification with Keras

Keras provides very convenient tools for fast protyping Machine Learning models, especially neural networks. You can pass metric functions when compiling a model, to evaluate the learnt models. However in the current version (after v2.

Privacy in Collaborative ML

On the concern of data privacy, we aim to develop algorithms towards learning accurate models privately from data.

Subspace Learning

Supervised learning on subspace data which could model real data like skeleton motion.