stat.ML · 2026-06-25 · No. 34

Machine Learning, 2026-06-25.

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

01 — The papers

6 entries
  1. 01

    When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification?

    Zhengchi Ma, Pengfei Lyu, Anru R. Zhang

    stat.ML · cs.LG

    Synthetic data augmentation is widely used to mitigate class imbalance, but its theoretical effects on score-based classification remain poorly understood. This paper develops a framework for characterizing when synthetic minority augmentation can improve threshold-integrated and threshold-optimized metrics, including AUROC, AUPRC, best-threshold balanced accuracy, and best-threshold \(\F_1\) score. We separate the effect of augmentation into...

    arxiv.org/abs/2606.26053 · PDF

  2. 02

    FedReLa: Imbalanced Federated Learning via Re-Labeling

    Guangzheng Hu, Patricia Menéndez, Feng Liu, Mingming Gong, Guanghui Wang, Liuhua Peng

    stat.ML · cs.CV · cs.LG

    Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation. The global class imbalance and cross-client data heterogeneity naturally coexist, and the mismatch between local and global imbalances exacerbates the performance degradation of the aggregated model. The agnosticism of global class distribution poses significant challenges for data-level methods, especially under extreme...

    arxiv.org/abs/2606.26037 · PDF

  3. 03

    Gaussian Mean Field Variational Inference can Overestimate Predictive Variance

    James Odgers, Ben Riegler, Siddharth Swaroop, Vincent Fortuin

    stat.ML · cs.LG

    Mean Field Variational Inference (MFVI) is widely understood to underestimate posterior variance. By analysing conjugate Bayesian Linear Regression (BLR), we show that this characterization is incomplete: while MFVI underestimates the variance in parameter space, it can overestimate the predictive variance compared to the exact posterior. We show that if the MFVI posterior underestimates predictive variances in some directions, it necessarily...

    arxiv.org/abs/2606.25745 · PDF

  4. 04

    Statistically Valid Hyperparameter Selection: From Tuning to Guarantees

    Amirmohammad Farzaneh, Osvaldo Simeone

    stat.ML · cs.IT · cs.LG · math.ST

    Hyperparameter selection is a critical step in the deployment of modern artificial intelligence systems, given the need to tune degrees of freedom such as inference-time parameters, implementation-level settings, and thresholds driving decision rules. Despite its practical importance, hyperparameter selection is typically performed using best-effort empirical methods such as grid search or Bayesian optimization, which provide no formal...

    arxiv.org/abs/2606.25601 · PDF

  5. 05

    Stabilizing black-box algorithms through task-oriented randomization

    Yali Wang, Zhaojun Wang

    stat.ML · cs.AI · cs.LG

    As black-box models become foundational to modern research, ensuring their stability is paramount for the realization of trustworthy artificial intelligence. The inherent diversity of inputs - ranging from structured Gaussian distributions to complex data with unknown structures - poses a significant challenge: how to stabilize black-box outputs while effectively leveraging available prior information. This paper introduces a task-oriented...

    arxiv.org/abs/2606.25269 · PDF

  6. 06

    Minimax PAC Bounds for Learning in Exogenous Contextual MDPs

    Corentin Pla, Hugo Richard, Marc Abeille, Vianney Perchet

    stat.ML · cs.LG

    We study PAC learning in tabular discounted Markov decision processes with exogenous i.i.d. contexts, with discount factor $γ$, finite state space $\mathcal X$, action space $\mathcal A$, and context space $\mathcal Z$. At each time step, a context is drawn independently from an unknown distribution $μ$ and revealed before the agent acts. This context may affect both rewards and transitions, while remaining uncontrolled by the agent....

    arxiv.org/abs/2606.25170 · PDF

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