Low-rank adaptation (LoRA) is a fine-tuning technique that can be applied to conditional
generative diffusion models. LoRA utilizes a small number of context examples to adapt the …

Diffusion models have emerged as effective distribution estimators in vision, language, and
reinforcement learning, but their use as priors in downstream tasks poses an intractable …

Divergent thinking, the cognitive process of generating diverse solutions, is a hallmark of
human creativity and problem-solving. For machines, sampling diverse solution trajectories …

Generative modeling of discrete data underlies important applications spanning text-based
agents like ChatGPT to the design of the very building blocks of life in protein sequences …

Predictive posterior densities (PPDs) are of interest in approximate Bayesian inference.
Typically, these are estimated by simple Monte Carlo (MC) averages using samples from the …

An effective approach for sampling from unnormalized densities is based on the idea of
gradually transporting samples from an easy prior to the complicated target distribution. Two …

This paper studies Generative Flow Networks (GFlowNets), which learn to sample objects
proportionally to a given reward function through the trajectory of state transitions. In this …

Monte Carlo methods, Variational Inference, and their combinations play a pivotal role in
sampling from intractable probability distributions. However, current studies lack a unified …

Bayes' rule naturally allows for inference refinement in a streaming fashion, without the need
to recompute posteriors from scratch whenever new data arrives. In principle, Bayesian …

In this work, we consider the problem of training a generator from evaluations of energy
functions or unnormalized densities. This is a fundamental problem in probabilistic …