stat.ML · 2026-09-22 · No. 121

Machine Learning, 2026-09-22.

7 new papers in stat.ML. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

7 entries
  1. 01

    JAREX: An Acquisition Function for Multi-Objective Algorithmic Process Characterization

    Xinyang Li, Kevin Stone, Ajit Vikram

    stat.ML · cs.LG

    Pharmaceutical process characterization is central to Quality by Design because it defines how variations in process parameters affect the ability to meet product quality specifications, thereby supporting proven acceptable ranges and robust manufacturing. In practice, however, characterization still relies largely on factorial design of experiments (DOE) approaches, which are inefficient for resolving multivariate pass/fail boundaries in...

    arxiv.org/abs/2609.24954 · PDF

  2. 02

    Identifying Representational Biases in Datasets Using PCA: A Max-Disparity Partition Framework

    Arjun KM, Shashi Jain

    stat.ML · cs.LG

    Principal Component Analysis (PCA) minimises aggregate reconstruction error, which can inadvertently represent majority subgroups with substantially higher fidelity than minority subgroups. Fairness-aware extensions of PCA correct this disparity but require group labels as input. We address the logically prior question: given only a data matrix, which binary partition of the data suffers the greatest representational disparity under a shared...

    arxiv.org/abs/2609.24556 · PDF

  3. 03

    Beyond Point Prediction: Artificial Representative Trees with Uncertainty

    Lea L. Mairhöfer, Silke Szymczak, Björn-Hergen Laabs, Tuwe Löfström-Cavallin

    stat.ML · cs.LG

    Random forests (RFs) predict well but are opaque, whereas single decision trees are interpretable but unstable. Artificial representative trees (ARTs) were developed as interpretable surrogate models for RFs, but their use as standalone prediction models with uncertainty quantification has not been systematically investigated. We combine ARTs with leaf-wise Mondrian conformal predictive systems (CPS), enabling a single tree to provide...

    arxiv.org/abs/2609.24528 · PDF

  4. 04

    Adversarially Robust PAC Learning with Optimal VC Rates

    Steve Hanneke, Amirreza Shaeiri

    stat.ML · cs.LG · math.ST

    We study the problem of \emph{adversarially robust} PAC learning. In this framework, the learner observes independent samples from an unknown distribution over $\mathcal{X} \times \{0,1\}$, as in classical PAC learning. However, given a perturbation map $\mathcal{U} : \mathcal{X} \to 2^{\mathcal{X}}$ known to the learner, the goal is to output, with high probability, a predictor that correctly classifies \emph{every} perturbation $z \in...

    arxiv.org/abs/2609.24260 · PDF

  5. 05

    OSCAR: Order-aware Scoring and Calibration for AI Rankings

    You Liu, Yue Liu, Quanchao Lu, Nick Shipilov

    stat.ML · cs.LG · stat.AP

    Judge-specific sensitivity is useful for aggregating pairwise LLM evaluations, but its interpretation depends on which systematic presentation effects the ranking model includes. We introduce OSCAR, an order-aware framework for scoring and calibrating AI rankings, and study position as one such effect. In released judgments from 18 evaluators, the all-response A-minus-B score difference ranges from $-63.11$ to $98.31$ percentage points....

    arxiv.org/abs/2609.24128 · PDF

  6. 06

    Model-Agnostic Feature Selection via LOCO-Guided Adaptive Minipatch Sampling

    Xuhui Liu, Lili Zheng

    stat.ML · cs.LG · stat.ME

    Black-box machine learning models increasingly deliver strong predictions, but extracting useful information from them, such as a set of important features, remains challenging. Existing model-agnostic methods primarily estimate feature importance or conduct inference on it rather than directly selecting features, whereas many feature selection methods are model-specific or rely on the model-X assumption. We introduce LOCO-guided Adaptive...

    arxiv.org/abs/2609.24126 · PDF

  7. 07

    Causal Bayesian Optimization: Foundations, Methods, and Applications

    Chenfeng Huang, Thuy T. Le, Zixuan Ma, Hien Tran

    stat.ML · cs.LG

    Causal Bayesian Optimization (CBO) combines causal inference with Bayesian optimization to enable sample-efficient intervention selection in systems with causal structure. This survey provides a systematic review of CBO through a unified BO-loop perspective, showing how causal assumptions shape intervention search spaces, surrogate models, acquisition functions, and decision policies. We organize existing methods by graph and system-knowledge...

    arxiv.org/abs/2609.24112 · PDF

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