arXiv Machine Learning By Michele Caprio, Katerina Papagiannouli, Siu Lun Chau, Sayan Mukherjee

Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features

Read the original on arXiv Machine Learning →

arXiv:2602. 19126v2 Announce Type: replace Abstract: We propose a robust Bayesian formulation of random feature (RF) regression that accounts explicitly for prior and likelihood misspecification via Huber-style contamination sets.

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arXiv Machine Learning
Jul 27

Smart predict-then-robustly-optimize

arXiv:2607. 21773v1 Announce Type: new Abstract: In this paper, we propose and study a robust variant of the smart predict-then-optimize approach that accounts for prediction shifts due to disturbance in the covariate feature space.

By Aakil Caunhye, Xuefei Lu, Belen Martin-Barragan
arXiv Machine Learning
Sep 24

Dirichlet Process Mixtures of Trees with Gaussian Process Splits: A Bayesian Nonparametric Framework with Posterior Contraction Rate

arXiv:2609. 27930v1 Announce Type: cross Abstract: We propose a Bayesian nonparametric mixture of regression trees with a Dirichlet process prior over tree-parameter pairs, enabling data-driven selection of ensemble size and unifying CART, BART, random forests, and boosting.

By Subhasish Basak, Anik Roy, Sourabh Bhattacharya