Daniel Andrade
Associate Professor
Hiroshima University
Associate Professor
Hiroshima University
Statistical Machine Learning Bayesian Statistics Natural Language Processing
As models become more complex, performing Bayesian inference is getting harder.
To tackle these new challenges, we currently explore two directions:
Variational inference using normalizing flows
Hybrid approaches that combine maximum-a-posterior solutions with MCMC sampling for Bayesian Neural Networks.
Slides of Invited Talk at BIRS, Slides of Tutorial at BIRS, Related Works
Accurate outlier detection is not only a necessary preprocessing step, but can itself give important insights into the data. However, especially, for non-linear regression the detection of outliers is non-trivial, and actually ambiguous.
Here we develop a new methodology for handling outlier for Gaussian Process Regression that is complementary to the standard solution of using a Student-t likelihood.
Deep learning, and in particular, large language models (LLMs) have become the dominant approach for text classification. However, explaining why the deep learning model decided a certain class is hardly possible. This is especially problematic for high-stake decisions in medical care.
As a solution, we develop new methods that can classify and analyze large amount of texts while providing simple explanations to the user.