Bayesian Learning
Approximation-Aware Bayesian Optimization
Our approach can be used with any decision-theoretic acquisition function and is readily compatible with trust region methods like TuRBO.
Active Learning with LLMs for Partially Observed and Cost-Aware Scenarios
Conducting experiments and collecting data for machine learning models is a complex and expensive endeavor, particularly when confronted with limited information. Typically, extensive experiments to obtain features and labels come with a significant acquisition cost, making it impractical to carry out all of them. Therefore, it becomes crucial to strategically determine what to acquire to maximize the predictive performance while minimizing costs.
Semi-Supervised Sparse Gaussian Classification: Provable Benefits of Unlabeled Data
The premise of semi-supervised learning (SSL) is that combining labeled and unlabeled data yields significantly more accurate models.
DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised h-transform
Most recent approaches are motivated heuristically and lack a unifying framework, obscuring connections between them. Further, they often suffer from issues such as being very sensitive to hyperparameters, being expensive to train or needing access to weights hidden behind a closed API.
