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Articles by Tin Lok James
[2611.09698] A Generalized Bayesian Approach to Clustering with Dimension Reduction
Generalized Bayes product partition models provide a probabilistic counterpart to loss-based clustering. They update a prior over partitions through an exponentiated clustering loss rather than a likelihood, so allocation uncertainty can be quantified without a full generative model for the data. However, their performance depends on the space in which the loss is evaluated.
Computer Science > Machine Learning
Individual fairness in machine learning requires that similar individuals receive similar predictions. Most existing definitions are pairwise in nature, imposing a Lipschitz constraint that must hold either globally across all pairs of data instances or locally within specified neighborhoods. In practice, however, such pairwise constraints may be overly restrictive and may not directly identify which individuals receive locally inconsistent predictions.
Computer Science > Machine Learning
[Submitted on 19 Mar 2026 (v1), last revised 14 Oct 2026 (this version, v2)] View PDF HTML (experimental) Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2603.18838 [cs.LG] (or arXiv:2603.18838v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.18838
[2105.06903] Posterior Regularization on Bayesian Hierarchical Mixture Clustering
[Submitted on 14 May 2021 ( v1 ), last revised 5 May 2023 (this version, v7)] Title:Posterior Regularization on Bayesian Hierarchical Mixture Clustering Download a PDF of the paper titled Posterior Regularization on Bayesian Hierarchical Mixture Clustering, by Weipeng Huang and 3 other authors Download PDF Abstract: Bayesian hierarchical mixture clustering (BHMC) improves traditionalBayesian hierarchical clustering by replacing conventional Gaussian-to-Gaussian kernels with a Hierarchical...
Fact Check: Analyzing Financial Events from Multilingual News Sources
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