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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.

168,694 papers · 148 categories

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8 results for “post-prediction”

Prediction De-Correlated Inference: A safe approach for post-prediction inferencestat.ME

A new method for safer statistical inference after predictions.

problem Statistical inference with pseudo-outcomes from machine learning predictions.

method Prediction De-Correlated Inference (PDC) framework.

result PDC consistently outperforms supervised methods and can adapt to any model.

statistical inference post-prediction machine learning pseudo-outcomes safe inference

2023-12-11abs ↗pdf ↗

A Moment-Based Generalization to Post-Prediction Inferencestat.ME

Extends post-prediction inference method for more accurate AI/ML data analysis.

problem Naively using AI/ML predictions as true observations leads to biased results.

method Extends Wang et al. method to relax assumptions and incorporate a scaling factor.

result Yields unbiased point estimates and proper coverage in simulations.

AI ML inference bias post-prediction

2025-07-12abs ↗pdf ↗

Assumption-Lean and Data-Adaptive Post-Prediction Inferencestat.ME

New method for valid inference from ML-predicted data.

problem Invalid scientific conclusions from ML-predicted outcomes.

method Assumption-Lean and Data-Adaptive Post-Prediction Inference (PSPA)

result Valid and powerful inference based on ML-predicted data.

machine learning post-prediction inference statistical validity data-adaptive

2023-11-23abs ↗pdf ↗

ELSA: Efficient Label Shift Adaptation through the Lens of Semiparametric Modelsstat.ML

ELSA efficiently adapts to label shift without post-prediction calibrations.

problem Domain adaptation with label shift across training and testing datasets.

method Moment-matching framework based on influence function geometry; solves linear systems for adaptation weights.

result ELSA estimator is

\sqrt{n}

-consistent and asymptotically normal, achieving state-of-the-art estimation performance.

label shift domain adaptation semiparametric models moment-matching influence function

2023-05-30abs ↗pdf ↗

Task-Agnostic Machine-Learning-Assisted Inferencestat.ML

Novel framework for ML-assisted inference valid for any statistical task.

problem Limited validity of existing methods for post-prediction inference.

method Introduces PSPS framework for task-agnostic ML-assisted inference.

result Valid and efficient inference for arbitrary ML models.

Machine Learning Statistical Inference Post-Prediction Task-Agnostic Software Framework

2024-05-30abs ↗pdf ↗

On the Predictability of non-CGM Diabetes Data for Personalized Recommendationcs.CY

With continuous glucose monitoring (CGM), data-driven models on blood glucose prediction have been shown to be effective in related work. However, such (CGM) systems are not always available, e.g., for a patient at home. In this work, we conduct a study on 9 patients and examine the online predictability of data-driven…

2018-08-19abs ↗pdf ↗

Online learning techniques for prediction of temporal tabular datasets with regime changesq-fin.CP

Modular pipeline improves stock portfolio prediction robustness under regime changes.

problem Overfitting in deep learning models for non-stationary datasets.

method Modular machine learning pipeline with GBDT models and online learning techniques.

result GBDT models with dropout show high performance, robustness, and generalisability.

stock prediction regime changes deep learning online learning robustness

2022-12-30abs ↗pdf ↗

Learning Fairness-aware Relational Structurescs.LG

The development of fair machine learning models that effectively avert bias and discrimination is an important problem that has garnered attention in recent years. The necessity of encoding complex relational dependencies among the features and variables for competent predictions require the development of fair, yet ex…

2020-02-21abs ↗pdf ↗