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 …

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 …

Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a
distribution over discrete objects proportional to their rewards. GFlowNets exhibit a …

GFlowNets are probabilistic models that sequentially generate compositional structures
through a stochastic policy. Among GFlowNets, temperature-conditional GFlowNets can …

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 …

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

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

Causal relationships underpin modern science and our ability to reason. Automatically
discovering useful causal relationships can greatly accelerate scientific progress and …

Estimating the structure of a Bayesian network, in the form of a directed acyclic graph (DAG),
from observational data is a statistically and computationally hard problem with essential …