7 papers
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…
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 meth…
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…
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…
Amortising over hyperparameters in Generalised Bayesian Inference
Laura Battaglia, Chris U. Carmona, Ross A. Haines +3
In Bayesian inference prior hyperparameters are chosen subjectively or estimated using empirical Bayes methods. Generalised Bayesian Inference (GBI) also has a learning rate hyperp…