Hugging Face Trending Papers July 13, 2026

Condition-Stratified Robustness Analysis of Post-Hoc Calibration Methods for Probabilistic Classifiers

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Post-hoc calibration is widely adopted to correct probability estimates from trained classifiers, yet most evaluations report aggregate performance without testing whether that performance holds across distinct operating conditions within a single dataset. We present a pre-registered, condition-stratified robustness analysis comparing temperature scaling (TEMP) and isotonic regression (ISO) across four controlled conditions (C1--C4).

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arXiv Machine Learning

Jul 14

Condition-Stratified Robustness Analysis of Post-Hoc Calibration Methods for Probabilistic Classifiers

arXiv:2607. 11542v1 Announce Type: new Abstract: Post-hoc calibration is widely adopted to correct probability estimates from trained classifiers, yet most evaluations report aggregate performance without testing whether that performance holds across distinct operating conditions within a single dataset.

By Gurdeep Singh Virdee

arXiv Machine Learning

Jul 21

The Calibration Channel Determines the Bayes-Error Proxy: An Exact Law for Temperature-Induced Distortion

arXiv:2607. 18162v1 Announce Type: new Abstract: The soft-label Bayes-error estimator beta(z) = E[min(z, 1-z)] of Ishida et al.

By Shreyas Pradeepkumar Khandale

arXiv Machine Learning

Aug 11

SoftMCC: An MCC-Brier Calibration Bridge for Threshold-Free Model Selection under Class Imbalance

arXiv:2608. 08984v1 Announce Type: new Abstract: Model selection for imbalanced binary classification often uses the Matthews correlation coefficient (MCC), but thresholding makes validation rankings threshold-dependent.

By \"Ozkan Canay

arXiv Machine Learning

Jul 16

Temperature Scaling Is Not Enough: Calibration Gaps Under Human Label Distributions

arXiv:2607. 13423v1 Announce Type: new Abstract: Temperature scaling is the dominant post-hoc calibration method in modern deep learning.

By Wisdom Dogah

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arXiv Machine Learning

1d ago

Guaranteed Adaptive Modality Acquisition: When the Policy Chooses Its Own Calibration Group

arXiv:2608. 15520v1 Announce Type: new Abstract: A multimodal system may begin inference holding only some of its inputs and may acquire the rest at a cost.

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Hugging Face Trending Papers

Aug 10

SoftMCC: An MCC-Brier Calibration Bridge for Threshold-Free Model Selection under Class Imbalance

Model selection for imbalanced binary classification often uses the Matthews correlation coefficient (MCC), but thresholding makes validation rankings threshold-dependent. SoftMCC is a post-training MCC validation framework on established probability-valued confusion counts, coupling an MCC-specific calibrated identity with a tie-aware, shared-pool selection protocol.

Related stories

Condition-Stratified Robustness Analysis of Post-Hoc Calibration Methods for Probabilistic Classifiers

The Calibration Channel Determines the Bayes-Error Proxy: An Exact Law for Temperature-Induced Distortion

SoftMCC: An MCC-Brier Calibration Bridge for Threshold-Free Model Selection under Class Imbalance

Temperature Scaling Is Not Enough: Calibration Gaps Under Human Label Distributions

Guaranteed Adaptive Modality Acquisition: When the Policy Chooses Its Own Calibration Group

SoftMCC: An MCC-Brier Calibration Bridge for Threshold-Free Model Selection under Class Imbalance