Combinatorial optimization (CO) problems are often NP-hard and thus out of reach for exact
algorithms, making them a tempting domain to apply machine learning methods. The highly …

Autoregressive large language models (LLMs) compress knowledge from their training data
through next-token conditional distributions. This limits tractable querying of this knowledge …

The recently proposed generative flow networks (GFlowNets) are a method of training a
policy to sample compositional discrete objects with probabilities proportional to a given …

We tackle the problem of sampling from intractable high-dimensional density functions, a
fundamental task that often appears in machine learning and statistics. We extend recent …

We consider the problem of sampling from a discrete and structured distribution as a
sequential decision problem, where the objective is to find a stochastic policy such that …

Generative Flow Networks (GFlowNets) are a new family of probabilistic samplers where an
agent learns a stochastic policy for generating complex combinatorial structure through a …

Phylogenetics is a branch of computational biology that studies the evolutionary
relationships among biological entities. Its long history and numerous applications …

We study the problem of training diffusion models to sample from a distribution with a given
unnormalized density or energy function. We benchmark several diffusion-structured …

We study the problem of training diffusion models to sample from a distribution with a given
unnormalized density or energy function. We benchmark several diffusion-structured …

Generative flow networks (GFlowNets) are sequential sampling models trained to match a
given distribution. GFlowNets have been successfully applied to various structured object …