A framework for sensitivity measures using scoring functions.
problem Constructing sensitivity measures for any elicitable functional.
method Score-based sensitivities constructed via consistent scoring functions.
result Demonstrated intuitive and desirable properties of score-based sensitivities.
Optimal score function estimation via empirical risk minimization
problem Estimating the score function of a probability measure on the flat torus from a sample
method Constraining the hypothesis space to a Sobolev ball
result Minimax estimation rates are achieved
The paper explores consistent scoring functions for quantiles and expectiles.
problem Evaluating and comparing forecasts using consistent scoring functions.
method Choquet representations and extremal scoring functions.
result Scoring functions for quantiles and expectiles can be represented as mixtures of extremal functions.
CNN scoring function predicts protein-ligand interactions.
problem Scoring protein-ligand interactions for drug discovery.
method Convolutional Neural Networks (CNN) for 3D protein-ligand interactions.
result CNN scoring function outperforms AutoDock Vina in ranking poses.
The paper introduces tools for designing responsible scoring mechanisms using unbiased function sampling.
problem Ensuring fairness and responsibility in scoring mechanisms used by data-driven systems.
method Unbiased function sampling and perturbation in the function space for designing responsible scoring mechanisms.
result A novel algorithm for approximating the construction of hyperplane arrangements, which is linear in the number of samples and hyperplanes.
Langevin dynamics fails to produce accurate samples even with small score function errors.
problem Robustness of Langevin dynamics to score function errors.
method Analysis of Langevin dynamics and score function errors.
result Langevin dynamics produces a distribution far from the target distribution in TV distance even with small L2 errors in the score function. This paper introduces and develops a novel variable importance score function in the context of ensemble learning and demonstrates its appeal both theoretically and empirically. Our proposed score function is simple and more straightforward than its counterpart proposed in the context of random forest, and by avoiding …
Study finds simple model-agreement scores perform well in various error estimation scenarios.
problem Evaluating model performance on unseen distributions using disparate scoring functions.
method Rigorously studied popular scoring functions (confidence, local manifold smoothness, model agreement) independently of mechanism choice.
result Simple model-agreement scores outperform confidence- and smoothness-based scores in realistic settings with compromised training data.
Score-fPINN tackles high-dimensional FPL equations using fractional score functions.
problem High-dimensional Fokker-Planck-Lévy equations with non-Brownian processes.
method Fractional score function and Physics-informed neural networks (PINN) to solve CoD and numerical overflow.
result Effective solution to high-dimensional FPL equations without fractional Laplacian.
New approach to score function in diffusion models using Malliavin calculus.
problem Estimating score function for complex data distributions.
method Combines Malliavin calculus with Bismut-type formula to derive exact score function expression.
result Derives exact, closed-form expression for score function in diffusion models.
DEEN learns energy and score functions from complex data.
problem Challenges in density estimation for high-dimensional data.
method Inference-free hierarchical framework using score matching and multilayer perceptrons.
result DEEN successfully learns energy and score functions from synthetic and high-dimensional data.
Proposes new nonlinear scoring functions to maximize pAUC in binary classification.
problem Inaccurate binary classification measures in biased prior probability settings.
method Introduces nonlinear scoring functions based on generative models and deep neural networks.
result Nonlinear scoring functions improve pAUC maximization compared to linear functions.
Improved score matching methods for estimating score functions and Hessians without high dimensionality.
problem Estimating score functions and Hessians efficiently in high-dimensional data.
method Implicit score matching and denoising score matching, leveraging Gagliardo-Nirenberg inequalities.
result Achieves convergence rates similar to denoising score matching and estimates Hessians without dimensionality issues.
Improves conformal prediction by combining multiple score functions and optimizing weights.
problem Limitations of single-score conformal predictors in multi-class classification.
method Combines multiple score functions and optimizes weights to minimize prediction set size.
result Consistently outperforms single-score conformal predictors while maintaining valid coverage.
Study proposes a differentiable surrogate loss function for optimizing Fβ score in binary classification with imbalanced data.
problem Non-differentiability of Fβ score makes it unsuitable for optimization by gradient-based learning. method Investigated relationship between Fβ score and loss functions, proposed a differentiable surrogate loss function. result Gradient paths of the proposed surrogate Fβ loss function approximate the gradient paths of the Fβ score. Algorithm optimizes biological sequences using bootstrapped training with a score-conditioned generator.
problem Optimizing biological sequences for a black-box score function.
method Bootstrapped training of score-conditioned generator (BootGen) algorithm.
result Our method outperforms competitive baselines on biological sequential design tasks.
New methods identify and score systemic risk measures accurately.
problem Identifying and scoring systemic risk measures accurately.
method Constructing oriented selective identification functions to induce a mixture representation of strictly consistent scoring functions.
result Demonstrated the applicability of the constructed functions through a comprehensive simulation study.
Researchers combined linear classifiers using score functions and found simple and trimmed averages to be the best combination strategies.
problem Combining linear classifiers using their score functions.
method Two score functions tested; four combination strategies investigated; comparison with majority voting and model averaging.
result Simple and trimmed average combination strategies were the best.
A new method assigns anomaly scores to features for better interpretation.
problem Interpreting anomaly scores from feature attributions.
method Proposes a characteristic function to attribute anomaly scores using Shapley value.
result Demonstrates the potential utility of the proposed attribution 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 ildeΘ(n−d+42) for d dimensions. Deep networks can approximate score functions in high-dimensional graphical models efficiently.
problem Approximation efficiency of score functions by deep neural networks in high-dimensional graphical models like Markov random fields.
method Variational inference denoising algorithms and efficient neural network representation.
result Efficient sample complexity bound for diffusion-based generative modeling when score functions are learned by deep neural networks.
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.
Proposes SD-KDE for density estimation using debiased kernel density with score-based adjustments.
problem Density estimation with bias in kernel density estimation.
method Adjusts data points by taking a step along the estimated score function, then applies standard KDE with modified bandwidth.
result Significantly reduces mean integrated squared error compared to standard Silverman KDE, especially with noisy score function estimates.
The paper tackles fairness in scoring functions for binary classification.
problem Fairness in scoring functions for binary classification tasks.
method Introduces ROC-based fairness constraints and learning algorithms.
result Generalization bounds and practical learning algorithms for fair scoring functions.
Paper develops consistent estimation of propensity scores for rare exposures.
problem Estimation of propensity score functions for rare exposures in oversampled cohorts.
method Flexible computational implementation using source population probability of exposure and observation weighting.
result Low empirical bias and variance for consistent propensity score function estimators.
Score function estimators improve k-subset sampling efficiency.
problem Efficiently sampling k-subsets in machine learning tasks. method Revisit score function estimators, using discrete Fourier transform and control variates.
result Efficient and unbiased gradient estimates for k-subset sampling. New maximum score estimators using ReLU functions and deep neural networks.
problem Estimating parameters in models with sign restrictions.
method ReLU-based maximum score criterion and DNN architecture.
result RMS estimator achieves n−s/(2s+1) convergence rate and asymptotic normality. The paper argues for using Neyman orthogonal score for balancing in debiased machine learning.
problem Debiased machine learning requires a proper approach to balance covariates.
method The paper advocates for using Riesz regression with basis functions of X for balancing.
result Covariate balancing is only valid when the score-relevant regression error is a function of covariates alone.
The paper explores elicitability of statistical functionals and introduces new conditions for higher-order elicitation.
problem Understanding and comparing forecasts of multi-dimensional statistical functionals.
method Develops conditions for strictly consistent scoring functions and introduces the concept of higher-order elicitation.
result Spectral risk measures with finite support are jointly elicitable when correct quantiles are added.
Improved hypothesis testing and change-point detection using diffusion-based methods.
problem Limited power of score-based hypothesis tests and change-point detection.
method Extending score-based Fisher divergence to diffusion-divergence by multiplying score functions with a matrix-valued function or weight matrix.
result Theoretical quantification and demonstration of optimal performance of diffusion-based algorithms.
The paper establishes bounds for score-matching in causal discovery and generative modeling.
problem Estimating causal relationships from data.
method Training a deep neural network to estimate the score function and applying it to causal discovery.
result Bounds on the error rate of causal discovery methods using score-matching.
New method generates novel samples from closed-form diffusion models.
problem Closed-form SGMs memorize training data and cannot generate novel samples.
method Explicitly smooth closed-form score, use nearest-neighbor estimator.
result Efficient method generates novel samples without training.
New method connects leverage scores and kernel density, revealing a decreasing relationship.
problem Understanding the relationship between leverage scores and kernel density.
method Introducing regularized Christoffel functions to study leverage scores for kernel methods.
result Quantitatively describes a decreasing relation between leverage score and population density for a broad class of kernels.
Proposes DNN for enhancing sound quality scores.
problem Improving sound quality assessment scores.
method Develops a DNN optimization scheme based on black-box optimization and policy gradient method.
result Significant increase in OSQA scores without minimizing MSE.
Diffusion models improve creativity by smoothing the score function, leading to interpolated data.
problem Improving creativity in diffusion models.
method Analyzing the effect of score smoothing on diffusion model dynamics.
result Score smoothing causes diffusion models to generate data that interpolate the training set.
Paper proposes an efficient causal discovery method with linear computational complexity.
problem Identifying causal relationships efficiently in large datasets.
method Approximate kernel-based generalized score function with low-rank technique and sampling algorithms.
result Significantly reduces computational costs while maintaining comparable accuracy.
Theory for deep neural network approximation of score function and its derivatives.
problem Handling data distributions with low-dimensional structure and unbounded support.
method Simultaneous approximation of the score function and its derivatives using deep neural networks.
result Approximation error bounds match literature but relax bounded support requirement.
Topological anomaly scores predict return curves in S&P 500 stocks
problem Detecting anomalies in financial time series
method BallMapper, decoder-conditional VAE, Function-on-Function regression
result Anomaly history carries predictive content for return curves
New methods for scoring function decomposition improve forecast evaluation.
problem Improving forecast evaluation and understanding forecast components.
method Linear recalibration of forecasts for miscalibration, discrimination, and uncertainty.
result Enhanced statistical power and deeper insights into forecast components.
A new activation function improves credit scoring accuracy for imbalanced datasets.
problem Imbalanced datasets in credit scoring lead to underestimation of misclassification costs.
method Introduces ASIG, an asymmetric adjusted Sigmoid function.
result ASIG-embedded classifier outperforms traditional classifiers across various imbalance ratios.
UCoS avoids forward model evaluations in sampling for large-scale linear inverse problems.
problem Efficient sampling from posterior distributions in large-scale linear inverse problems.
method UCoS approach that learns a task-dependent score function offline and uses affine transformations to derive the conditional score.
result UCoS eliminates the need for forward model evaluations during sampling, making it more efficient.
Develops graphical tools for evaluating Expected Shortfall forecasts.
problem Lack of intuitive or empirical guidance for choosing scoring functions.
method Murphy diagrams and hypothesis tests for forecast evaluation.
result Shows which forecast methods dominate under relevant scoring functions.
Robust quickest change detection method for unknown score functions.
problem Detecting changes in data streams with unknown pre- and post-change distributions.
method Selects least-favorable distributions and robustifies score-based detection algorithm.
result Demonstrates improved performance in simulations.
Method solves Bayesian inverse problems in function space without assuming log-concavity.
problem Bayesian inverse problems in infinite-dimensional nonlinear settings.
method Score-based diffusion models as a prior, Langevin-type MCMC on function spaces.
result Provable convergence bound for posterior sampling, dependent on score approximation.
Novel estimator reduces diffusion model variance.
problem High variance in score function estimation for diffusion models.
method Uses nearest neighbour samples to estimate the score function.
result Significant decrease in variance, leading to improved model performance.
New method reveals true causal functions in nonlinear time series, not just scores.
problem Causal discovery in nonlinear time series often uses scalar edge scores, which hide true function-valued causal influence.
method Formalized function-valued causal influence for additive, contribution-decomposable architectures. Introduced a practical framework based on ICE for estimating causal response functions directly from trained models.
result Edges with indistinguishable scalar scores can exhibit qualitatively different functional behaviors.
A new loss function ED simplifies training energy-based models without scores.
problem Training energy-based models is computationally expensive.
method Energy Discrepancy (ED) loss function that does not rely on scores or MCMC.
result ED effectively interpolates between score matching and negative log-likelihood.
Proposes a new scoring function for linear classifiers to improve object positioning in feature space.
problem Lack of information about relative positions of recognized objects in feature space.
method Calculates a scoring function based on object distance from decision boundary and class centroid.
result Demonstrates effectiveness of the proposed method compared to other ensemble algorithms on multiple datasets.