stat.ML · 2026-07-20 · No. 59

Machine Learning, 2026-07-20.

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

01 — The papers

5 entries
  1. 01

    Cluster-Aware Matching via Laplacian Optimal Transport

    Gabriel Samberg, YoonHaeng Hur, Yuehaw Khoo, Nir Sharon

    stat.ML · cs.LG · math.NA · stat.ME

    In many applications of matching, the point clouds to be matched are not merely unstructured sets of points but rather samples from distributions with an intrinsic cluster structure. In such cases, as individual points are often interchangeable within a coherent region, finding a robust region-to-region alignment is more desirable than establishing a precise point-to-point correspondence. To this end, we propose a novel approach for...

    arxiv.org/abs/2607.16178 · PDF

  2. 02

    Deep and Probabilistic Models for Gene Regulatory Network Inference

    Claudia Skok Gibbs

    stat.ML · cs.LG · stat.AP · stat.ME

    Gene regulatory networks (GRNs) link transcription factor (TF) proteins to their target genes, yet reconstructing these networks from genome-wide data remains challenging under practical and methodological constraints. Many methods couple modeling assumptions to a specific inference procedure and rely on heuristic model selection, while evaluation is constrained by incomplete reference networks and point-estimate outputs that lack...

    arxiv.org/abs/2607.16053 · PDF

  3. 03

    Which Hyperparameters Matter? A Game-Theoretic Framework for Interpretable Hyperparameter Sensitivity Analysis

    Nyi Nyi Aung, Heepeom Shin, Abigail Lawlor, Adrian Stein

    stat.ML · cs.LG · stat.CO

    This work presents a game-theoretic framework for interpretable hyperparameter-objective interaction analysis rather than proposing a new optimization algorithm. In the proposed framework, Shapley Effects are employed for global sensitivity analysis, while Pareto front sets are utilized to identify effective hyperparameter configurations and support early-stage model evaluation. The resulting analysis reveals which players (hyperparameters)...

    arxiv.org/abs/2607.15884 · PDF

  4. 04

    Retraining Seeks Stable Signals

    Moritz Hardt

    stat.ML · cs.LG

    Predictive models deployed at scale influence future data, a phenomenon called performativity. And there is always one way to cope: Train the model on new data, deploy it again, and repeat. This process, called retraining or repeated risk minimization, creates a feedback loop between model and data that real-world learning systems can't avoid. Results on performative prediction shed light on this dynamic: If the model's influence on the data...

    arxiv.org/abs/2607.15623 · PDF

  5. 05

    Design-Based Supervised Learning with Noisy Human Labels

    Robert Chew, Matthew R. Williams

    stat.ML · cs.AI · cs.LG · stat.AP

    Researchers increasingly use automated classifiers to label unstructured data for statistical analysis. Existing rectification methods can correct errors in these automated labels using a probability-sampled audit set, but they usually treat the audit labels as correct. In practice, human audit labels are often noisy, and only some audited items are reviewed by an expert or adjudicator. We propose Partially Adjudicated Design-Based Supervised...

    arxiv.org/abs/2607.15455 · PDF

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