3 citations · 4 across the 10 of their papers we have counts for
10 papers
Bessel-Debiased Pseudo-Marginal MCMC for Generalised Bayesian Inference
Yingkai Lu, Jeong Eun Lee, Geoff K. Nicholls
Generalised Bayesian inference uses weights of the form even when the loss is only estimated. Exponentiating an unbiased loss estimate changes the target, a…
Hierarchical Partial-Order Models for Ranking
Dongqing Li, Geoff K. Nicholls, Jeong Eun Lee +2
Rank aggregation combines information from ordered lists ranking items by preference. Classical parametric models for such data, including the Mallows and Plackett-Luce models, ass…
A Differentiable Bayesian Relaxation for Latent Partial-Order Inference
Dongqing Li, Geoff K. Nicholls, Shiyi Sun +1
Many ranking and agent trace datasets are recorded as linear orders even though their latent structure is only partially ordered. This is especially common in agent and workflow tr…
De-Linearizing Agent Traces: Bayesian Inference of Latent Partial Orders for Efficient Execution
Dongqing Li, Zheqiao Cheng, Geoff K. Nicholls +1
AI agents increasingly execute procedural workflows as sequential action traces, which obscures latent concurrency and induces repeated step-by-step reasoning. We introduce BPOP, a…
Amortized Simulation-Based Inference in Generalized Bayes via Neural Posterior Estimation
Shiyi Sun, Geoff K. Nicholls, Jeong Eun Lee
Generalized Bayesian Inference (GBI) tempers a loss with a temperature to mitigate overconfidence and improve robustness under model misspecification, but existing GBI metho…
Bayesian inference for the learning rate in Generalised Bayesian inference
Jeong Eun Lee, Sitong Liu, Geoff K. Nicholls
In Generalised Bayesian Inference (GBI), the learning rate and hyperparameters of the loss must be estimated. These inference-hyperparameters can't be estimated jointly with the ot…