Probabilistic forecasts in the form of probability distributions over future events have become popular in several fields of statistical science. The dissimilarity between a probability forecast and an outcome is measured by a loss function (scoring rule). Popular example of scoring rule for continuous outcomes is the …
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48 results for “continuous ranked probability score”
Paper evaluates CRPS for extreme event forecasts, finding it unsuitable.
problem Verifying probabilistic forecasts of extreme events is challenging.
method Formal framework using extreme value theory to assess CRPS as a random variable.
result CRPS is unsuitable for extreme event verification.
New scoring rules for multivariate distributions and level sets.
problem Evaluating forecast accuracy for multivariate distributions and level sets.
method Theoretical framework for scoring rules, decomposition of multivariate scoring functions, numerical algorithm for computation.
result New scoring functions for multivariate distributions and level sets, including density and cumulative distribution level sets.
We formulate a supervised learning problem, referred to as continuous ranking, where a continuous real-valued label Y is assigned to an observable r.v. X taking its values in a feature space and the goal is to order all possible observations x in by means of a scoring function $s:\mathcal{X}…
Deep learning improves PV generation quantile forecasting.
problem Accurate probabilistic forecasting of PV generation.
method Developed an encoder-decoder deep learning model for multi-output quantile PV forecasting.
result The model improves forecast quality and computational efficiency.
The consultative papers for the Basel II Accord require rating systems to provide a ranking of obligors in the sense that the rating categories indicate the creditworthiness in terms of default probabilities. As a consequence, the default probabilities ought to present a monotonous function of the ordered rating catego…
Kernel quadrature improves CRPS estimation for probabilistic time-series forecasting.
problem Intractable integrations in CRPS evaluation metrics lead to improper rankings of forecasting models.
method Introduced kernel quadrature approach for unbiased CRPS estimation and scalable computation.
result Our approach consistently outperforms existing CRPS estimators.
Proposes MVG-CRPS for robust multivariate forecasting.
problem Outliers in multivariate forecasting lead to significant errors.
method Integrates CRPS for MVG distributions, optimizing with MVG-CRPS.
result Improves robustness, accuracy, and uncertainty quantification.
Paper discusses extending Gini score for tied rankings and case weights.
problem Extending Gini score for tied rankings and case weights.
method Discuss and adapt Gini score for ties and case weights.
result Gini score can be used for tied rankings and case weights.
New methods ensure feature importance rankings are correct with high probability.
problem Stability issues in feature importance scores due to random sampling.
method Hypothesis testing-based techniques to assess and verify the stability of top-ranked features.
result Ensures the most important features are correct with high-probability guarantees.
Deep neural networks forecast financial return distributions accurately.
problem Forecasting probability distributions of financial returns.
method Used 1D CNN and LSTM architectures with custom loss functions to optimize distribution parameters.
result LSTM with skewed Student's t distribution outperformed classical models in multiple evaluation metrics.
The question of aggregating pair-wise comparisons to obtain a global ranking over a collection of objects has been of interest for a very long time: be it ranking of online gamers (e.g. MSR's TrueSkill system) and chess players, aggregating social opinions, or deciding which product to sell based on transactions. In mo…
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…
On the sensitivity of machine-learned probabilistic weather forecast models to scale-aware scoring rulesphysics.ao-ph
Study compares different scoring rules for machine-learned weather forecasts, finding scale-awareness improves forecast realism.
problem Improving the accuracy of machine-learned probabilistic weather forecasts.
method Comparison of scoring rules (CRPS, fair global energy score, graph energy score) and analysis of their impact on forecast field spectra.
result Scale-awareness improves forecast realism, particularly in the tropics.
This paper is devoted to the bipartite ranking problem, a classical statistical learning task, in a high dimensional setting. We propose a scoring and ranking strategy based on the PAC-Bayesian approach. We consider nonlinear additive scoring functions, and we derive non-asymptotic risk bounds under a sparsity assumpti…
Where machine-learned predictive risk scores inform high-stakes decisions, such as bail and sentencing in criminal justice, fairness has been a serious concern. Recent work has characterized the disparate impact that such risk scores can have when used for a binary classification task. This may not account, however, fo…
CNNs improve wind speed forecasts in the Netherlands.
problem Limited spatial patterns in current post-processing methods.
method Convolutional Neural Networks (CNNs) for spatial wind speed information.
result CNNs produce better probabilistic forecasts with higher Brier skill scores.
We consider the problem of exact recovery of any matrix of rank from a small number of observed entries via the standard nuclear norm minimization framework. Such low-rank matrices have degrees of freedom . We show that any arbitrary low-rank matrices can be recovered exa…
Study improves distributional regression evaluation with CRPS, finding optimal rates of convergence.
problem Improving probabilistic forecasts in meteorology using distributional regression.
method Extends theoretical properties of CRPS evaluation to include covariates and finite sample sizes, analyzing convergence rates for different methods.
result Optimal minimax rate of convergence for distributional regression methods is achieved by k-nearest neighbor and kernel methods.
Probabilistic survival predictions from models trained with Maximum Likelihood Estimation (MLE) can have high, and sometimes unacceptably high variance. The field of meteorology, where the paradigm of maximizing sharpness subject to calibration is popular, has addressed this problem by using scoring rules beyond MLE, s…
VLM judges rank well but score poorly; task difficulty and annotation quality affect interval width.
problem VLMs as judges lack reliability indicators in multimodal evaluations.
method Conformal prediction using score-token log-probabilities.
result Evaluation uncertainty is task-dependent, affecting interval width and reliability.
A graph neural network improves multivariate post-processing of ensemble forecasts.
problem Systematic biases in ensemble forecasts and loss of dependencies across forecast dimensions.
method A composite-Loss Graph Neural Network (dualGNN) trained with a composite loss function combining ES and VS.
result The dualGNN outperforms traditional methods in multivariate verification metrics and captures spatial relationships.
CTRNNs improve blood glucose forecasting in ICU, outperforming traditional models.
problem Forecasting blood glucose in ICU with irregular measurements.
method Continuous time autoregressive recurrent neural networks (CTRNNs) using neural ODE or neural flow layers.
result CTRNNs generally outperform traditional autoregressive models in probabilistic forecasting of blood glucose.
Predictive models learned from historical data are widely used to help companies and organizations make decisions. However, they may digitally unfairly treat unwanted groups, raising concerns about fairness and discrimination. In this paper, we study the fairness-aware ranking problem which aims to discover discriminat…
Transforms distance-based outlier scores into interpretable probabilistic estimates.
problem Difficult interpretation of distance-based outlier scores.
method Generic transformation of scores into probabilistic estimates using distance probability distributions.
result Probabilistic transformation improves interpretability without impacting detection performance.
Investment strategies for rank-dependent utility agents are derived in a continuous-time market.
problem Time inconsistency in rank-dependent utility models.
method Study of consistent planners seeking intra-personal equilibrium strategies.
result Explicit final wealth profile replicating equilibrium strategies, with scaling function derived.
Null-Calibrated Conformal Selection via Target-Membership Scores
problem Identifying test candidates whose unknown responses fall in a target region while controlling the false discovery rate
method Membership-score-based conformal selection
result Finite-sample valid null p-values
A new measure PC allows fair comparison of AIWP and NWP models.
problem Fair comparison of AIWP and NWP model outputs.
method Postprocessing deterministic model outputs with isotonic distributional regression (IDR) and calculating PC as mean CRPS of postprocessed forecasts.
result The GraphCast model outperforms the ECMWF HRES model in WeatherBench 2 data.
Paper improves uncertainty estimation in LLM-as-a-judge systems.
problem Improving uncertainty estimation in LLM-as-a-judge frameworks.
method Generalised probabilistic modelling and improved uncertainty estimates.
result Proposed uncertainty estimates significantly improve system efficiency.
The study establishes risk bounds for distributional regression estimators.
problem Estimating distributional regression models with nonparametric methods.
method Theoretical bounds for CRPS and MSE are derived for convex and non-convex constraints.
result Theoretical risk bounds are validated through experiments on simulated and real data.
A new model for supervised learning to rank using gradient estimation.
problem Improving supervised learning to rank methods.
method Proposes a new model with a gradient estimator for categorical variables.
result Achieves better or comparable results compared to existing methods.
Estimates score function from data with optimal rate in high dimensions.
problem Estimating the score function of an unknown probability distribution from data.
method Empirical Bayes smoothing with a Gaussian kernel.
result Optimal rate of estimation for dimensions.
Convergence Analysis for General Probability Flow ODEs of Diffusion Models in Wasserstein Distancesstat.ML
Paper analyzes convergence of ODE samplers in Wasserstein distances.
problem Limited theoretical understanding of convergence properties of probability flow ODEs.
method Convergence analysis for general probability flow ODEs in 2-Wasserstein distance.
result First non-asymptotic convergence analysis for probability flow ODE samplers.
Paper proposes a scoring function for detecting anomalies in large datasets.
problem Detecting outliers in large, feature-rich datasets.
method Binary classification problem with a two-sample linear rank statistic.
result Empirical results show the effectiveness of the proposed scoring function.
Researchers use LLMs to judge other LLMs, but this study provides a new geometric perspective to understand when it works.
problem The challenge of evaluating LLMs using other LLMs as judges, considering both aleatoric and epistemic uncertainties.
method A geometric perspective on ranking LLM candidates using probability simplices, analyzing conditions for identifiable rankings and designing Bayesian priors.
result Geometric analysis reveals that rankings based on LLM judges are robust in many but not all datasets, emphasizing the importance of modeling epistemic uncertainty.
A common problem in machine learning is to rank a set of n items based on pairwise comparisons. Here ranking refers to partitioning the items into sets of pre-specified sizes according to their scores, which includes identification of the top-k items as the most prominent special case. The score of a given item is defi…
We describe algorithms for learning Bayesian networks from a combination of user knowledge and statistical data. The algorithms have two components: a scoring metric and a search procedure. The scoring metric takes a network structure, statistical data, and a user's prior knowledge, and returns a score proportional to …
Machine Learning on graph-structured data is an important and omnipresent task for a vast variety of applications including anomaly detection and dynamic network analysis. In this paper, a deep generative model is introduced to capture continuous probability densities corresponding to the nodes of an arbitrary graph. I…
A new method predicts precipitation distributions from ensemble forecasts.
problem Improving accuracy and calibration of precipitation forecasts.
method Distributional regression U-Nets for postprocessing ensemble precipitation forecasts.
result Competitive performance in continuous ranked probability score, especially for heavy precipitation.
We explain theoretically a curious empirical phenomenon: "Approximating a matrix by deterministically selecting a subset of its columns with the corresponding largest leverage scores results in a good low-rank matrix surrogate". To obtain provable guarantees, previous work requires randomized sampling of the columns wi…
The paper ranks items based on top choices in multiway comparisons.
problem Ranking items based on top choices in multiway comparisons.
method Uniform sampling scheme, statistical rates of convergence, asymptotic normality, maximum likelihood estimator, Gaussian multiplier bootstrap.
result Proposed inference framework for ranking items through maximum pairwise difference statistic.
LITE models improve query-document relevance with learnable late interactions.
problem Improving query-document relevance with lower latency and storage.
method Proposes learnable late-interaction models (LITE) that use factorized query and document embeddings followed by a learnable scorer.
result Empirically, LITE outperforms previous late-interaction models in re-ranking tasks.
A hybrid machine learning model predicts soccer match scores for FIFA Women's World Cups.
problem Forecasting soccer match scores for FIFA Women's World Cups.
method Combining bookmaker consensus and team strength parameters in a random forest model.
result The model favors the defending champion USA over the host France.
Study evaluates multivariate forecasting scoring rules and proposes new copula-based ones.
problem Evaluating and improving multivariate probabilistic forecasting methods.
method Analysis and comparison of existing scoring rules, development of copula-based scoring rules, simulation studies, and real data analysis.
result Proposed copula-based scoring rules provide strong distinction between models with correct and incorrect dependency structures.
This paper improves matrix completion by leveraging element importance and non-uniform sampling.
problem The challenge of completing low-rank matrices from noisy, subsampled measurements.
method Employing leverage scores to characterize element importance and devising a biased sampling procedure.
result Theoretical and empirical evidence shows that a smaller number of entries (about ) can recover a low-rank matrix with noise.
In this paper, we consider the problem of column subset selection. We present a novel analysis of the spectral norm reconstruction for a simple randomized algorithm and establish a new bound that depends explicitly on the sampling probabilities. The sampling dependent error bound (i) allows us to better understand the …
Two new methods generate probabilistic forecasts of individual treatment effects.
problem Generating probabilistic forecasts of individual treatment effects for risk-aware decision-making.
method Proposes CCT and CMC meta-learners combining conformal predictive systems with analytic convolution or Monte Carlo sampling.
result Achieve probabilistically calibrated predictive distributions and performant continuous ranked probability scores.
We study the active learning problem of top- ranking from multi-wise comparisons under the popular multinomial logit model. Our goal is to identify the top- items with high probability by adaptively querying sets for comparisons and observing the noisy output of the most preferred item from each comparison. To ac…