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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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5 results for “H-calibration”

Calibration and Consistency of Adversarial Surrogate Lossescs.LG

Study on calibration and consistency of adversarial surrogate losses.

problem Designing robust classifiers with theoretical guarantees.

method Extensive analysis of H-calibration and H-consistency of adversarial surrogate losses.

result Some convex loss functions and supremum-based convex losses are not H-calibrated for important hypothesis sets.

Adversarial robustness Surrogate losses H-calibration H-consistency Generalized linear models

2021-04-19abs ↗pdf ↗

h-calibration: Rethinking Classifier Recalibration with Probabilistic Error-Bounded Objectivecs.LG

Proposes h-calibration for improving miscalibrated probability outputs of neural networks.

problem Improving reliability of probability outputs from neural networks.

method Probabilistic learning framework for calibration, including a simple yet effective post-hoc algorithm.

result Significantly better performance than traditional methods, validated by experiments.

neural networks miscalibration recalibration probability outputs post-hoc methods

2025-06-22abs ↗pdf ↗

A Finer Calibration Analysis for Adversarial Robustnesscs.LG

We improve adversarial robustness calibration analysis for broader hypothesis sets.

problem Improving calibration for adversarial robustness in machine learning.

method A finer definition of calibration for adversarial robustness.

result Our results cover most common hypothesis sets in machine learning.

adversarial robustness calibration machine learning hypothesis sets

2021-05-04abs ↗pdf ↗

Fundamental Novel Consistency Theory:

H

-Consistency Boundscs.LG

Study on $H$ -consistency bounds for machine learning surrogates.

problem Estimating target loss error relative to surrogate loss error in machine learning.

method Developed

H

-consistency bounds for various surrogates and loss functions.

result Stronger guarantees than existing methods, offering distribution-dependent and -independent bounds.

machine learning consistency theory surrogate loss target loss binary classification

2025-12-28abs ↗pdf ↗

\mathscr{H}

-Consistency Estimation Error of Surrogate Loss Minimizerscs.LG

Study of estimation errors in surrogate loss minimizers, providing stronger guarantees than existing methods.

problem Estimation errors in surrogate loss minimizers for various hypothesis sets.

method Detailed study of

\mathscr{H}

-consistency estimation error bounds, proving general theorems for distribution-dependent and independent settings.

result Explicit bounds for zero-one and adversarial losses, showing enhancements under distributional assumptions.

estimation errors surrogate loss minimizers hypothesis set distribution-dependent distribution-independent

2022-05-16abs ↗pdf ↗