Hierarchical data is ubiquitous in the empirical sciences and is most commonly analyzed with generalized linear mixed-effects models (GLMMs). Bayesian inference for GLMMs yields calibrated uncertainty...
arXiv:2609.24422v1 Announce Type: new
Abstract: Hierarchical data is ubiquitous in the empirical sciences and is most commonly analyzed with generalized linear mixed-effects models (GLMMs). Bayesian...
By Alex Kipnis, Marcel Binz, Eric Schulz
arXiv:2610.01269v1 Announce Type: cross
Abstract: Bayesian Optimisation (BO) is a powerful framework for the optimisation of expensive black-box functions, but typically requires refitting a surrogat...
By Luca Geminiani, Nadja Klein
arXiv:2606. 10287v1 Announce Type: new Abstract: Evaluating Knowledge Graph Completion (KGC) models remains challenging because standard assessment relies on isolated rank-based metrics such as MRR, Hits$@$k, and Mean Rank, which often produce conflicting model orderings across datasets.
By Haji Gul, Ajaz Ahmad Bhat
arXiv:2609.06941v1 Announce Type: new
Abstract: Causal effect estimation asks how an outcome would change under an intervention, and medicine, economics, and public policy all treat it as a foundatio...
By Haohao Zhou
arXiv:2607. 11956v1 Announce Type: cross Abstract: Data Shapley is the standard principled answer to which training points are worth what, and its k-nearest-neighbor (KNN) specialization is the version deployed in practice: the exact estimator shipped by toolkits such as pyDVL and OpenDataVal.
By Zongye Lyu