Our lab values the usability of our work by the community. Therefore, we work hard to create publicly available code for all our research. We’ve launched a new lab GitHub, where you can find great explanations for several feature selection methods. Other projects are documented below.

• Knowledge Editing in Language Models via Adapted Direct Preference Optimization
• Anomaly Detection with Variance Stabilized Density Estimation
• Domain generalizable multiple domain clustering
• Feature selection using stochastic gates
• Unsupervised feature selection based on a gated Laplacian
• Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features
• Geometry based data generation
• Variational Diffusion Autoencoders with Random Walk Sampling
• Local conformal Autoencoder
• Alignment free identification of clones in B cell receptor repertoires