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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.

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7 results for “calibeating”

Proper Calibeatingecon.TH

New concept of proper-calibeating extends classic calibrated forecasts to proper scoring rules.

problem Defining and extending calibrated forecasts to proper scoring rules.

method Extending the concepts of calibrated and calibeating forecasts to proper scoring rules and proving their properties.

result Proper-calibration always implies calibration, but proper-calibeating does not necessarily imply calibeating.

calibration proper scoring rules decision making no regret forecast

2026-05-26abs ↗pdf ↗

Online Platt Scaling with Calibeatingcs.LG

Online Platt Scaling adapts to varying data distributions.

problem Adapting Platt scaling to non-i.i.d. settings with distribution drift.

method Combines Platt scaling with online logistic regression and calibeating.

result OPS+calibeating method is guaranteed to be calibrated for adversarial outcomes.

Platt scaling online calibration distribution drift calibeating logistic regression

2023-04-28abs ↗pdf ↗

"Calibeating": Beating Forecasters at Their Own Gameecon.TH

A new method calibrates forecasts without sacrificing expertise.

problem Forecasters' calibration scores can be manipulated to appear expert.

method Deterministic and stochastic online procedures to calibrate forecasts.

result Calibration can be achieved without losing expertise.

forecast calibration expertise Brier score online procedure stochastic procedure

2022-09-11abs ↗pdf ↗

Calibeating for general proper losses: A Bregman divergence approachcs.LG

This work broadens calibeating to various proper losses using Bregman divergence.

problem Calibration for a wide range of proper losses.

method Regret minimization and Bregman divergence approach.

result U-calibration results for a family of Tsallis losses with logarithmic regret and dimension independence.

calibration proper losses Bregman divergence Tsallis losses logarithmic regret

2026-05-17abs ↗pdf ↗

Calibeating for general proper losses: A Bregman divergence approachcs.LG

This work generalizes calibeating for a broader range of proper losses using Bregman divergence.

problem Calibration for a wide range of proper losses beyond Brier and log loss.

method Regret minimization based on Bregman divergence for a family of proper losses.

result U-calibration results for a family of Tsallis losses with logarithmic regret and dimension independence.

calibration proper losses Bregman divergence Tsallis losses logarithmic regret

2026-05-17abs ↗pdf ↗

A Comparison of Modeling Preprocessing Techniquesstat.ME

This paper compares preprocessing techniques for XGBoost models on various data sets.

problem Improving predictive performance of XGBoost models through optimal data preprocessing.

method Comparison of feature selection, categorical handling, and null imputation methods.

result XGBoost importance by gain is the most consistent and highest-performing method for feature selection.

XGBoost preprocessing feature selection categorical encoding missing data

2023-02-23abs ↗pdf ↗

Calibeating Prediction-Powered Inferencestat.ML

Calibrated Prediction-Powered Inference improves semisupervised mean estimation by calibrating prediction scores.

problem Semisupervised mean estimation with a small labeled sample and a large unlabeled sample, and miscalibrated prediction models.

method Calibrated Prediction-Powered Inference (Calibeating) post-hoc calibrates the prediction score on the labeled sample before using it for semisupervised estimation.

result Calibrated Prediction-Powered Inference can improve the original score both as a predictor of the outcome and as a regression adjustment for semisupervised inference.

semisupervised learning calibration prediction models mean estimation inference

2026-04-23abs ↗pdf ↗