Bayesian Inference
Meta-Learning Universal Priors Using Non-Injective Change of Variables
Critical in this context is the prior knowledge accumulated from related tasks.
Amortized Bayesian Experimental Design for Decision-Making
For example, in personalized medical diagnostics, a model is built based on historical data to facilitate an optimal treatment for a new patient (Bica et al., 2021).
Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time
Diffusion-based generative models, as first introduced by Sohl-Dickstein et al. (2015), have shown
Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows
In a slight abuse of notation, we use ฯ to denote both the target distribution and its density.
Principled Bayesian Optimisation in Collaboration with Human Experts
We consider a setup where experts provide advice on the next query point through binary accept/reject recommendations (labels).
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data
Kai Helli, David Schnurr, Noah Hollmann, Samuel Mรผller, Frank Hutter
Until now, no tabular method has consistently outperformed classical supervised learning, which ignores these shifts.
Stopping Bayesian Optimization with Probabilistic Regret Bounds
Monte Carlo stopping rules in a manner that is both sample efficient and robust to estimation error.
Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors
Recent advancements in solving Bayesian inverse problems have spotlighted de-noising diffusion models (DDMs) as effective priors. Although these have great potential, DDM priors yield complex posterior distributions that are challenging to sample.