Accurate calibration of probabilistic predictive models learned is critical for many practical prediction and decision-making tasks. There are two main categories of methods for building calibrated classifiers. One approach is to develop methods for learning probabilistic models that are well-calibrated, ab initio. The…
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48 results for “Non-parametric Calibration”
The task of calibration is to retrospectively adjust the outputs from a machine learning model to provide better probability estimates on the target variable. While calibration has been investigated thoroughly in classification, it has not yet been well-established for regression tasks. This paper considers the problem…
New test assesses probabilistic model calibration without expensive approximations.
problem Assessing calibration of probabilistic models with scores.
method Kernel Calibration Conditional Stein Discrepancy (KCCSD) test using new score-based kernels.
result Control over type-I error with improved scalability and efficiency.
A set of probabilistic predictions is well calibrated if the events that are predicted to occur with probability p do in fact occur about p fraction of the time. Well calibrated predictions are particularly important when machine learning models are used in decision analysis. This paper presents two new non-parametric …
Many applications of classification methods not only require high accuracy but also reliable estimation of predictive uncertainty. However, while many current classification frameworks, in particular deep neural networks, achieve high accuracy, they tend to incorrectly estimate uncertainty. In this paper, we propose a …
New method calibrates MQHawkes model using non-parametric approach, identifying cross-Hawkes and cross-leverage effects.
problem Calibrating complex Hawkes processes with non-parametric methods.
method Non-parametric calibration using General Method of Moments on coarse-grained MQHawkes model.
result Identification of cross-Hawkes and cross-leverage effects in futures markets.
Develops a calibration prediction interval for non-parametric regression and neural networks.
problem Lack of accurate conditional prediction in regression settings.
method Calibration Prediction Interval (cPI) using Deep Neural Networks (DNN) or kernel methods.
result Asymptotically valid coverage rate and high probability of coverage rate with large sample sizes.
In many classification problems it is desirable to output well-calibrated probabilities on the different classes. We propose a robust, non-parametric method of calibrating probabilities called SplineCalib that utilizes smoothing splines to determine a calibration function. We demonstrate how applying certain transforma…
Method calibrates local volatility and stochastic short rate models for equity-rate dynamics.
problem Joint calibration of local volatility and stochastic short rate models.
method Iterative approach using semimartingale optimal transport.
result Demonstrated performance on market data using European SPX options and cap interest rate options.
Evaluating probabilistic classifiers: Reliability diagrams and score decompositions revisitedstat.ME
Improves reliability diagrams for probabilistic forecasts.
problem Lack of stability in reliability diagrams hampered their use.
method CORP approach using non-parametric isotonic regression and PAV algorithm.
result Improved reliability diagrams with statistical consistency and reproducibility.
There has been much recent interest in application of the pool-adjacent-violators (PAV) algorithm for the purpose of calibrating the probabilistic outputs of automatic pattern recognition and machine learning algorithms. Special cost functions, known as proper scoring rules form natural objective functions to judge the…
A novel Hawkes Process model captures order sizes in LOBs, improving fit quality and market impact studies.
problem Capturing the variability in order sizes in Limit Order Books (LOBs).
method Compound Hawkes Process with time-varying parameters and non-parametric calibration.
result Improved fit quality and empirical market impact function replication.
In this work, we consider the problem of estimating a behaviour policy for use in Off-Policy Policy Evaluation (OPE) when the true behaviour policy is unknown. Via a series of empirical studies, we demonstrate how accurate OPE is strongly dependent on the calibration of estimated behaviour policy models: how precisely …
Binary classification is highly used in credit scoring in the estimation of probability of default. The validation of such predictive models is based both on rank ability, and also on calibration (i.e. how accurately the probabilities output by the model map to the observed probabilities). In this study we cover the cu…
Improving self-calibrationastro-ph.IM
Response calibration is the process of inferring how much the measured data depend on the signal one is interested in. It is essential for any quantitative signal estimation on the basis of the data. Here, we investigate self-calibration methods for linear signal measurements and linear dependence of the response on th…
In recent work on both generative and discriminative score to log-likelihood-ratio calibration, it was shown that linear transforms give good accuracy only for a limited range of operating points. Moreover, these methods required tailoring of the calibration training objective functions in order to target the desired r…
Calibration of Local Volatility Models with Stochastic Interest Rates using Optimal Transportq-fin.MF
Method calibrates stock price models with stochastic interest rates using optimal transport.
problem Calibrating stock price models with stochastic interest rates.
method Non-parametric, semimartingale optimal transport, solving a fully non-linear Hamilton-Jacobi-Bellman equation.
result Fully calibrated model closest to a reference model in a defined cost function.
In this paper we refine the process of computing calibration functions for a number of multiclass classification surrogate losses. Calibration functions are a powerful tool for easily converting bounds for the surrogate risk (which can be computed through well-known methods) into bounds for the true risk, the probabili…
Learning accurate probabilistic models from data is crucial in many practical tasks in data mining. In this paper we present a new non-parametric calibration method called \textit{ensemble of near isotonic regression} (ENIR). The method can be considered as an extension of BBQ, a recently proposed calibration method, a…
This paper describes a flexible and tractable bottom-up dynamic correlation modelling framework with a consistent stochastic recovery specification. The stochastic recovery specification only models the first two moments of the spot recovery rate as its higher moments have almost no contribution to the loss distributio…
Two methods improve calibration of probabilistic classifiers, especially for multi-class problems.
problem Improving calibration of probabilistic classifiers, especially for multi-class problems.
method Two techniques: reduced calibration and class-wise calibration.
result Class-wise reduced calibration algorithms reduce prediction and per-class calibration errors.
NeuralSurv models survival analysis with Bayesian uncertainty.
problem Capturing time-varying risk relationships in survival analysis.
method Two-stage data-augmentation scheme, mean-field variational algorithm, coordinate-ascent updates, locally linearized Bayesian neural network.
result Delivers superior calibration compared to state-of-the-art models.
A new method constructs smooth, arbitrage-free option surfaces efficiently.
problem Creating smooth, arbitrage-free option surfaces efficiently.
method Non-parametric approach using strictly positive 'discrete local volatility' variables.
result First construction of smooth, strictly arbitrage-free option price surfaces.
Calibrates Hawkes models for market events, revealing power-law feedback kernels.
problem Estimating the influence of past events and price changes on future market events.
method Proposes a calibration procedure for Quadratic Hawkes models, analyzing the kernel components.
result Empirically calibrated kernel components reveal power-law behavior, suggesting system near critical point.
New neural network models extreme value distributions with preserved shape constraints.
problem Modeling multivariate extreme value distributions with preserved shape constraints.
method d-max-decreasing neural network architecture for non-parametric calibration and generation of MEVs.
result The proposed architecture approximates the dependence structure of MEVs at parametric rate and preserves essential shape constraints.
This paper tackles the problem of selecting among several linear estimators in non-parametric regression; this includes model selection for linear regression, the choice of a regularization parameter in kernel ridge regression, spline smoothing or locally weighted regression, and the choice of a kernel in multiple kern…
Deep neural network (DNN) regression models are widely used in applications requiring state-of-the-art predictive accuracy. However, until recently there has been little work on accurate uncertainty quantification for predictions from such models. We add to this literature by outlining an approach to constructing predi…
BoC probe assesses neural network confidence coherence, revealing architecture-specific uncertainty.
problem Poor calibration and OOD detection in neural networks.
method Bag-of-Coins (BoC) probe compares softmax confidence to pairwise dominance probabilities.
result BoC reveals clear ID/OOD separation for some architectures but not others.
Researchers use Gaussian Process Regression to improve accuracy of a low-cost hot-wire anemometer.
problem Improving accuracy of low-cost hot-wire anemometers in varying temperatures.
method Probabilistic calibration using Gaussian Process Regression.
result The method provides good performance in estimating actual wind speeds, including uncertainty.
We present an arbitrage-free non-parametric yield curve prediction model which takes the full (discretized) yield curve as state variable. We believe that absence of arbitrage is an important model feature in case of highly correlated data, as it is the case for interest rates. Furthermore, the model structure allows t…
Adaptive uncertainty quantification improves black-box model predictions in generative AI.
problem Improving uncertainty quantification for black-box models in generative AI.
method Adaptive partitioning and local calibration of conformity scores.
result Local tightening of uncertainty sets with adaptive bands.
Spatially weighted conformal prediction improves uncertainty quantification in house price models.
problem Uncertainty quantification in automated valuation models with spatial dependencies.
method Survey and demonstration of various spatially weighted approaches to adjust conformal prediction confidence sets.
result Spatially weighted CP makes confidence sets more consistently calibrated across geographical regions.
This work improves interpretability and calibration of complex-valued neural networks using Newton-Puiseux analysis.
problem Insufficient interpretability and probability calibration of complex-valued neural networks.
method Newton-Puiseux framework to examine local decision geometry, fitting a polynomial surrogate and factorizing it using Newton-Puiseux expansions.
result Enhanced Expected Calibration Error in ECG and wireless modulation datasets compared to uncalibrated softmax and standard post-hoc baselines.
Flexible model captures commodity skews with maturity effects.
problem Capturing market skew in commodity futures with maturity effects.
method Non-parametric extension with leverage functions, calibrated using Monte Carlo simulation.
result Model accurately captures market smile and implied variance accumulation.
Treeffuser predicts tabular data distributions using gradient-boosted trees.
problem Probabilistic prediction with flexible, non-parametric models.
method Gradient-boosted trees for score estimation in conditional diffusion model.
result Treeffuser outperforms existing methods in probabilistic prediction tasks.
New deep Cox mixture model improves survival analysis performance.
problem Challenges in survival analysis due to censoring and healthcare applications.
method Learning mixtures of Cox regressions with deep neural networks for hazard ratios and non-parametric baseline hazard.
result Our approach outperforms classical and modern survival analysis methods, especially in minority demographics.
In order to study the geometry of interest rates market dynamics, Malliavin, Mancino and Recchioni [A non-parametric calibration of the HJM geometry: an application of Itô calculus to financial statistics, {\it Japanese Journal of Mathematics}, 2, pp.55--77, 2007] introduced a scheme, which is based on the Fourier Seri…
WRSE predicts dynamic survival distributions in ICU patients.
problem Dynamic assessment of ICU patient mortality risk.
method Non-parametric weighted-resolution ensemble model combining binary classifiers.
result Competitive results with state-of-the-art models, reducing training time.
Framework for fast CAT calibration and administration using AutoML and IRT.
problem Calibrating and administering large-scale CAT tests with limited data.
method AutoIRT (AutoML + IRT) for calibration, BanditCAT for administration.
result Framework successfully launched new item types on DET practice test.
Conditional independence testing is a fundamental problem underlying causal discovery and a particularly challenging task in the presence of nonlinear and high-dimensional dependencies. Here a fully non-parametric test for continuous data based on conditional mutual information combined with a local permutation scheme …
Goal-oriented adaptive sampling under random field modelling of response probability distributionsstat.ME
The study optimizes sampling in complex systems with probabilistic response distributions.
problem Calibrating and optimizing complex systems with probabilistic response distributions.
method Non-parametric Bayesian approach to modeling spatial fields of probability distributions, introducing adaptive sampling strategies.
result Adaptive sampling strategies improve system evaluations by guiding focus towards key features.
BSLP is a two-dimensional dynamic model of interacting portfolio-level loss and spread (more exactly, loss intensity) processes. The model is similar to the top-down HJM-like frameworks developed by Schonbucher (2005) and Sidenius-Peterbarg-Andersen (SPA) (2005), however is constructed as a Markovian, short-rate intens…
STRAND: A single representation for hypothesis testing and vectorisation of persistence diagrams
problem Comparing persistence diagrams
method Survival topological representation analysis
result Non-parametric two-sample test with calibrated Type I error and high power
Proposes method for eliciting non-parametric joint priors using normalizing flows.
problem Learning complex non-parametric joint priors for model parameters.
method Expert elicitation combined with normalizing flows for generative modeling.
result Framework supports elicitation of both parametric and non-parametric priors.
We consider an investor, whose portfolio consists of a single risky asset and a risk free asset, who wants to maximize his expected utility of the portfolio subject to managing the Value at Risk (VaR) assuming a heavy tailed distribution of the stock prices return. We use a stochastic maximum principle to formulate the…
Many real-world regression problems demand a measure of the uncertainty associated with each prediction. Standard decision forests deliver efficient state-of-the-art predictive performance, but high-quality uncertainty estimates are lacking. Gaussian processes (GPs) deliver uncertainty estimates, but scaling GPs to lar…
Adversarially robust machine learning has received much recent attention. However, prior attacks and defenses for non-parametric classifiers have been developed in an ad-hoc or classifier-specific basis. In this work, we take a holistic look at adversarial examples for non-parametric classifiers, including nearest neig…
DSVGD improves federated learning with fewer communication rounds.
problem Federated learning scalability and trustworthiness.
method Distributed Stein Variational Gradient Descent (DSVGD) for non-parametric Bayesian inference.
result DSVGD achieves comparable accuracy and scalability to other methods, with well-calibrated predictions.