Papers scoring rule
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Aligned Textual Scoring Rules
Scoring rules elicit probabilistic predictions from a strategic agent by scoring the prediction against a ground truth state. A scoring rule is proper if, from the agent's perspective, reporting the true belief maximizes…
scoring ruleh-calibration: Rethinking Classifier Recalibration with Probabilistic Error-Bounded Objective
Deep neural networks have demonstrated remarkable performance across numerous learning tasks but often suffer from miscalibration, resulting in unreliable probability outputs. This has inspired many recent works on mitig…
scoring ruleEnforcing tail calibration when training probabilistic forecast models
Probabilistic forecasts are typically obtained using state-of-the-art statistical and machine learning models, with model parameters estimated by optimizing a proper scoring rule over a set of training data. If the model…
scoring ruleEnd-to-End Probabilistic Framework for Learning with Hard Constraints
We present a general purpose probabilistic forecasting framework, ProbHardE2E, to learn systems that can incorporate operational/physical constraints as hard requirements. ProbHardE2E enforces hard constraints by exploit…
Probabilistic Time Series Forecastingscoring ruleTime Series ForecastingUncertainty QuantificationThe Disparate Effects of Partial Information in Bayesian Strategic Learning
We study how partial information about scoring rules affects fairness in strategic learning settings. In strategic learning, a learner deploys a scoring rule, and agents respond strategically by modifying their features …
Fairnessscoring ruleLikelihood-Free Variational Autoencoders
Variational Autoencoders (VAEs) typically rely on a probabilistic decoder with a predefined likelihood, most commonly an isotropic Gaussian, to model the data conditional on latent variables. While convenient for optimiz…
Decoderscoring ruleRelationship between Hölder Divergence and Functional Density Power Divergence: Intersection and Generalization
In this study, we discuss the relationship between two families of density-power-based divergences with functional degrees of freedom -- the H\"{o}lder divergence and the functional density power divergence (FDPD) -- bas…
scoring ruleA Stable and Strategy-Proof Controlled School Choice Mechanism with Integrated and Flexible Rules
We examine a controlled school choice model where students are categorized into different types, and the distribution of these types within a school influences its priority structure. This study provides a general framew…
scoring ruleDistributional Diffusion Models with Scoring Rules
Diffusion models generate high-quality synthetic data. They operate by defining a continuous-time forward process which gradually adds Gaussian noise to data until fully corrupted. The corresponding reverse process progr…
scoring ruleConsistency Checks for Language Model Forecasters
Forecasting is a task that is difficult to evaluate: the ground truth can only be known in the future. Recent work showing LLM forecasters rapidly approaching human-level performance begs the question: how can we benchma…
Language ModelingLanguage Modellingmodelscoring ruleGeneralized Bayesian deep reinforcement learning
Bayesian reinforcement learning (BRL) is a method that merges principles from Bayesian statistics and reinforcement learning to make optimal decisions in uncertain environments. As a model-based RL method, it has two key…
Deep Reinforcement Learningreinforcement-learningReinforcement Learningscoring rule+1Harnessing Superclasses for Learning from Hierarchical Databases
In many large-scale classification problems, classes are organized in a known hierarchy, typically represented as a tree expressing the inclusion of classes in superclasses. We introduce a loss for this type of supervise…
scoring ruleEfficient pooling of predictions via kernel embeddings
Probabilistic predictions are probability distributions over the set of possible outcomes. Such predictions quantify the uncertainty in the outcome, making them essential for effective decision making. By combining multi…
Ensemble Learningscoring ruleSurvival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks
When dealing with right-censored data, where some outcomes are missing due to a limited observation period, survival analysis -- known as time-to-event analysis -- focuses on predicting the time until an event of interes…
scoring ruleStochastic OptimizationSurvival AnalysisConcentrated Superelliptical Market Maker
An automated market maker where the price can cross the zero bound into the negative price domain with applications in electricity, energy, and derivatives markets is presented. A unique feature involves the ability to s…
scoring ruleMVG-CRPS: A Robust Loss Function for Multivariate Probabilistic Forecasting
Multivariate Gaussian (MVG) distributions are central to modeling correlated continuous variables in probabilistic forecasting. Neural forecasting models typically parameterize the mean vector and covariance matrix of th…
Probabilistic Time Series Forecastingscoring ruleTime Series ForecastingUncertainty QuantificationEfficient Training of Neural Stochastic Differential Equations by Matching Finite Dimensional Distributions
Neural Stochastic Differential Equations (Neural SDEs) have emerged as powerful mesh-free generative models for continuous stochastic processes, with critical applications in fields such as finance, physics, and biology.…
Computational Efficiencyscoring ruleScoring rule nets: beyond mean target prediction in multivariate regression
Probabilistic regression models trained with maximum likelihood estimation (MLE), can sometimes overestimate variance to an unacceptable degree. This is mostly problematic in the multivariate domain. While univariate mod…
Formregressionscoring ruleSensitivitySuperior Scoring Rules for Probabilistic Evaluation of Single-Label Multi-Class Classification Tasks
This study introduces novel superior scoring rules called Penalized Brier Score (PBS) and Penalized Logarithmic Loss (PLL) to improve model evaluation for probabilistic classification. Traditional scoring rules like Brie…
Model OptimizationModel SelectionMulti-class Classificationscoring rule+1Bayesian Autoregressive Online Change-Point Detection with Time-Varying Parameters
Change points in real-world systems mark significant regime shifts in system dynamics, possibly triggered by exogenous or endogenous factors. These points define regimes for the time evolution of the system and are cruci…
Change Point Detectionscoring ruleTime Series