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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.

168,742 papers · 148 categories

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6 results for “post-calibration”

Propensity score models are better when post-calibratedstat.ME

Post-calibration improves the accuracy of causal effect estimation.

problem Improperly calibrated propensity scores lead to inaccurate causal effect estimation.

method Performed a simulation study to assess the impact of post-calibration on causal effect estimation.

result Post-calibration reduces the error in estimating the average treatment effect, especially for expressive uncalibrated statistical estimators.

propensity scores causal inference post-calibration average treatment effect simulation study

2022-11-02abs ↗pdf ↗

Bayesian Neural Network Versus Ex-Post Calibration For Prediction Uncertaintycs.LG

Bayesian neural networks outperform calibrated neural networks for tabular data.

problem Uncertainty in neural network predictions for tabular data.

method Bayesian neural networks vs. post-hoc calibration methods.

result Bayesian neural networks yield competitive performance compared to calibrated neural networks.

Bayesian neural networks uncertainty quantification calibration methods tabular data neural networks

2022-09-29abs ↗pdf ↗

Decision from Suboptimal Classifiers: Excess Risk Pre- and Post-Calibrationcs.LG

The study quantifies decision-making risks from suboptimal classifiers and proposes methods to reduce these risks.

problem Excess risk in decision-making from suboptimal probabilistic classifiers.

method Analytical expressions and upper/lower bounds for excess risk, calibration curve estimation, grouping loss estimator.

result Identifies regimes where recalibration alone or post-training is more effective.

Decision Making Excess Risk Probabilistic Classifiers Calibration Grouping Loss

2025-03-23abs ↗pdf ↗

No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacs.LG

Paper proposes CCVR to improve classifier calibration in federated learning with non-IID data.

problem Improving classifier calibration in federated learning with non-IID data.

method Proposes CCVR, a simple algorithm that adjusts the classifier using virtual representations.

result CCVR achieves state-of-the-art performance on popular federated learning benchmarks.

federated learning non-IID data classifier calibration virtual representations CIFAR-10

2021-06-09abs ↗pdf ↗

JUCAL: Jointly Calibrating Aleatoric and Epistemic Uncertainty in Classification Tasksstat.ML

JUCAL jointly calibrates aleatoric and epistemic uncertainties in classifier ensembles.

problem Misrepresentation of predictive uncertainty due to unbalanced aleatoric and epistemic uncertainties.

method Joint Uncertainty Calibration (JUCAL) that jointly calibrates two constants to weight and scale uncertainties.

result Significantly outperforms state-of-the-art calibration methods across various text classification tasks.

uncertainty calibration classification ensemble methods aleatoric uncertainty epistemic uncertainty

2026-02-23abs ↗pdf ↗

When Reasoning Fails: Evaluating 'Thinking' LLMs for Stock Predictionq-fin.ST

Thinking LLMs struggle with stock prediction, especially as data complexity increases.

problem Evaluating the performance of 'thinking' LLMs in stock prediction, especially under varying levels of cross-sectional complexity.

method Rolling 48m/1m walk-forward evaluation, comparing direct LLMs, TLLMs, and classical learners on cross-sectional ranking loss, MSE, and backtests with transaction costs.

result TLLMs' ranking quality deteriorates as cross-sectional complexity grows, while direct LLMs remain stable.

stock prediction thinking LLMs cross-sectional complexity walk-forward evaluation transaction costs

2025-11-05abs ↗pdf ↗