Learning Graphical Models
ODE-based Recurrent Model-free Reinforcement Learning for POMDPs
In partially observable (PO) environments, how to infer unseen information from raw observations puzzled the agents.
BayesTune: Bayesian Sparse Deep Model Fine-tuning
Deep learning practice is increasingly driven by powerful foundation models (FM), pre-trained at scale and then fine-tuned for specific tasks of interest.
Prior-itizing Privacy: A Bayesian Approach to Setting the Privacy Budget in Differential Privacy
When releasing outputs from confidential data, agencies need to balance the analytical usefulness of the released data with the obligation to protect data subjects'
ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design
Addressing these challenges requires scale. To that end we introduce ProteinGym, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design.
A Bayesian Approach To Analysing Training Data Attribution In Deep Learning
Training data attribution (TDA) techniques find influential training data for the model's prediction on the test data of interest.