We propose a general and scalable approximate sampling strategy for probabilistic models
with discrete variables. Our approach uses gradients of the likelihood function with respect …

Bayesian causal discovery aims to infer the posterior distribution over causal models from
observed data, quantifying epistemic uncertainty and benefiting downstream tasks …

We propose discrete Langevin proposal (DLP), a simple and scalable gradient-based
proposal for sampling complex high-dimensional discrete distributions. In contrast to Gibbs …

Entity resolution is the process of merging and removing duplicate records from multiple
data sources, often in the absence of unique identifiers. Bayesian models for entity …

Sampling approaches like Markov chain Monte Carlo were once popular for combinatorial
optimization, but the inefficiency of classical methods and the need for problem-specific …

The ability to engineer novel proteins with higher fitness for a desired property would be
revolutionary for biotechnology and medicine. Modeling the combinatorially large space of …

Optimal scaling has been well studied for Metropolis-Hastings (MH) algorithms in
continuous spaces, but a similar understanding has been lacking in discrete spaces …

Bayesian approaches for learning deep neural networks (BNN) have been received much
attention and successfully applied to various applications. Particularly, BNNs have the merit …

A long-standing goal of machine-learning-based protein engineering is to accelerate the
discovery of novel mutations that improve the function of a known protein. We introduce a …

Discrete diffusion modeling is a promising framework for modeling and generating data in
discrete spaces. To sample from these models, different strategies present trade-offs …