Trustworthy Generation

Pytorch source code and data for tabular transformers for modeling multivariate time series data, showcasing card transactions data synthetic generation and analysis. 

Code for an efficient computational method for attribute-controlled generation of molecules, which leverages guidance from classifiers trained on an informative latent space of molecules modeled using a deep generative autoencoder.

Code for training fair classifiers across different modalities such as tabular, language and image data, using fair mixup augmentation as a regularizer.

Code for unbalanced Sobolev Descent for generating unbalanced data with birth and death processes. 

Code for designing protein sequences conditioned on a specific target 3D fold using a novel transformer-based generative framework.

Code for non-linear feature selection and provable false discovery rate control using generative models and hold out randomized testings.