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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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6 results for “decision-calibrated”

Dimension-Free Decision Calibration for Nonlinear Loss Functionscs.LG

New algorithms achieve decision calibration without sample complexity dependent on feature dimension.

problem Achieving decision calibration for nonlinear loss functions with polynomial sample complexity.

method Developed smooth relaxation of decision calibration, enabling dimension-free algorithms.

result Efficient algorithms post-process predictors to satisfy decision calibration without worsening accuracy.

decision calibration nonlinear loss functions dimension-free algorithms smooth relaxation

2025-04-22abs ↗pdf ↗

Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibrationstat.ML

New approach calibrates predictions for better decision-making.

problem Achieving reliable predictions for multi-class problems is hard.

method Introduces decision calibration, a new approach to calibrate predictions.

result Designs a recalibration algorithm that makes predictions reliable for decision-making.

calibration decision-making multi-class machine learning

2021-07-12abs ↗pdf ↗

Decision-Calibrated Conformal Uncertainty for Pacing Decisions in Streaming Advertisingstat.ML

Develop a decision-calibrated conformal framework for pacing decisions in streaming advertising.

problem Pacing decisions in streaming advertising

method Develop a decision-calibrated conformal framework

result The proposed score is the smallest valid uncertainty measure that uniformly protects all deployable pacing policies.

pacing forecasting uncertainty conformal decision-calibrated

2026-06-08abs ↗pdf ↗

Decision-calibrated prediction sets for robust power system operationsmath.OC

Decision-calibrated prediction sets improve power system operations by reducing unnecessary costs.

problem Balancing operating costs and reliability in power systems with renewable uncertainty.

method Learn conditional prediction sets as sub-level sets of norm-based score functions, calibrate uncertainty sets based on reliability of downstream decisions.

result Decision-calibrated sets lead to more efficient operations with smaller uncertainty sets and lower costs compared to standard coverage-based calibration.

power systems renewable uncertainty robust optimization conformal risk control reserve scheduling

2026-06-01abs ↗pdf ↗

Robust Decision Making with Partially Calibrated Forecastsstat.ML

The paper addresses decision making with partially calibrated forecasts, offering a robust approach.

problem Developing a decision-making strategy for forecasts that are only partially calibrated.

method A minimax approach to mapping predictions to actions, considering worst-case distributions.

result The minimax optimal decision rule is to trust predictions and act accordingly, even for partially calibrated forecasts.

calibration decision making forecast minimax robustness

2025-10-27abs ↗pdf ↗

When accurate prediction models yield harmful self-fulfilling propheciesstat.ME

Prediction models can harm patients even when accurate, leading to self-fulfilling prophecies.

problem Prediction models can lead to harmful decisions that worsen patient outcomes.

method Formal characterization of harmful prediction models and analysis of their impact.

result Well-calibrated models are ineffective for decision-making as they do not change the data distribution.

prediction models harmful decisions self-fulfilling prophecies medical decisions calibration

2023-12-02abs ↗pdf ↗