arXiv research

Smooth calibration improves forecast reliability even with leaked information.

New calibration energy measures deviation from calibrated geometry, enabling mean curvature flow in infinite volumes.

Unified framework for generalized Venn and Venn-Abers calibration for reliable prediction.

Proposes a method to create shorter, more accurate prediction intervals.

Every graph can be represented as a singular set of a special surface.

New calibration bands for various distributions improve testing for auto-calibration.

ICP improves prediction intervals for continuous outcomes at lower computational cost.

The paper investigates how calibrating propensity scores improves DML estimates of average treatment effects.

T-Cal tests model calibration with a minimax optimal test.

Observing prices of European put and call options, we calibrate exponential Lévy models nonparametrically. We discuss the efficient implementation of the spectral estimation procedures for Lévy models of finite jump activity as well as for self-decomposable Lévy models. Based on finite sample variances, confidence inte…

New method calibrates LV surfaces for exotic derivatives with smoother, more stable Greeks.

Confidence intervals and joint confidence sets are constructed for the nonparametric calibration of exponential Lévy models based on prices of European options. To this end, we show joint asymptotic normality in the spectral calibration method for the estimators of the volatility, the drift, the jump intensity and the …

A new method calibrates value predictions in offline RL to improve reliability.

Calibration of stochastic local volatility (SLV) models to their underlying local volatility model is often performed by numerically solving a two-dimensional non-linear forward Kolmogorov equation. We propose a novel finite volume (FV) discretization in the numerical solution of general 1D and 2D forward Kolmogorov eq…

Self-calibrating conformal prediction improves interval efficiency and offers a practical alternative.

Boosting trees can test necessary conditions for regression model calibration.

Study non-monotonic loss functions in CRC, achieving valid risk control with large calibration samples.

The paper calibrates SPX and VIX options using optimal transport.

This work proves $L_2$-regularized ERM controls smCE without post-hoc correction.

New method turns any regression model into a calibrated probabilistic model.

Null-Calibrated Conformal Selection via Target-Membership Scores

Feature selection is a standard approach to understanding and modeling high-dimensional classification data, but the corresponding statistical methods hinge on tuning parameters that are difficult to calibrate. In particular, existing calibration schemes in the logistic regression framework lack any finite sample guara…

The Bass model is calibrated to vanilla options using a fixed-point equation.

CP4SBI improves the calibration of credible sets in SBI models.

New algorithm achieves online calibration in polynomial time for high-dimensional problems.

Proves existence and uniqueness of calibrated LSV model.

The paper proposes a method to calibrate evidential clustering using bootstrapped finite mixture models.

We provide yet another proof of the existence of calibrated forecasters; it has two merits. First, it is valid for an arbitrary finite number of outcomes. Second, it is short and simple and it follows from a direct application of Blackwell's approachability theorem to carefully chosen vector-valued payoff function and …

Trimming helps in conformal prediction when it separates anomaly scores.

Time-series calibrations often suggest that the GARCH diffusion model could also be a suitable candidate for option (risk-neutral) calibration. But unlike the popular Heston model, it lacks a fast, semi-analytic solution for the pricing of vanilla options, perhaps the main reason why it is not used in this way. In this…

The paper studies multi-curve interest rate models and their consistency and finite-dimensional realizations.

Framework calibrates ML models for risk control in various tasks.

Optimal multiclass U-calibration error found to be Θ(√KT).

TA-CQR predicts regression intervals with exact coverage, splitting miscoverage between endpoints.

Two methods improve Gaussian process predictive distributions' calibration.

We propose nonparametric methods for individual calibration in regression models.

We lay down an elementary yet fundamental lemma concerning a finite algebraicness property of a smooth map from an Azumaya/matrix manifold with a fundamental module to a smooth manifold. This gives us a starting point to build a synthetic (synonymously, $C^{\infty}$-algebraic) symplectic geometry and calibrated geometr…

New framework calibrates decision robustness using inverse conformal risk control.

New method calibrates stochastic reduced-order models from data efficiently.

Conformal Bayes under label shift: post-hoc calibration vs. in-training adaptation

Exact distribution of split conformal prediction coverage found.

The paper improves conformal prediction by analyzing the beta law of conditional coverage.

The paper calibrates a model to market quotes efficiently and arbitrage-free.

Paper proposes a diagnostic tool for evaluating model performance out-of-sample.

Study shows pooling scores for conformal prediction distorts group coverage.

In this paper, we study a semi-martingale optimal transport problem and its application to the calibration of Local-Stochastic Volatility (LSV) models. Rather than considering the classical constraints on marginal distributions at initial and final time, we optimise our cost function given the prices of a finite number…

Better uncertainty estimates for neural networks using Gaussian process priors.

New method extends conformal prediction to multivariate settings using optimal transport.