4 papers
Learning Generation Orders for Masked Discrete Diffusion Models via Variational Inference
David Fox, Sam Bowyer, Song Liu +3
Masked discrete diffusion models (MDMs) are a promising new approach to generative modelling, offering the ability for parallel token generation and therefore greater efficiency th…
Massively Parallel Expectation Maximization For Approximate Posteriors
Thomas Heap, Sam Bowyer, Laurence Aitchison
Bayesian inference for hierarchical models can be very challenging. MCMC methods have difficulty scaling to large models with many observations and latent variables. While variatio…
Position: Don't Use the CLT in LLM Evals With Fewer Than a Few Hundred Datapoints
Sam Bowyer, Laurence Aitchison, Desi R. Ivanova
Rigorous statistical evaluations of large language models (LLMs), including valid error bars and significance testing, are essential for meaningful and reliable performance assessm…
Using Autodiff to Estimate Posterior Moments, Marginals and Samples
Sam Bowyer, Thomas Heap, Laurence Aitchison
Importance sampling is a popular technique in Bayesian inference: by reweighting samples drawn from a proposal distribution we are able to obtain samples and moment estimates from…