scoring rule
22 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in scoring rule
Most implemented papers
NGBoost: Natural Gradient Boosting for Probabilistic Prediction
NGBoost generalizes gradient boosting to probabilistic regression by treating the parameters of the conditional distribution as targets for a multiparameter boosting algorithm.
Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors
We show that in the context of object detection, training variance networks with negative log likelihood (NLL) can lead to high entropy predictive distributions regardless of the correctness of the output mean.
A Spectral Energy Distance for Parallel Speech Synthesis
google-research/google-research
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NeurIPS 2020
Speech synthesis is an important practical generative modeling problem that has seen great progress over the last few years, with likelihood-based autoregressive neural models now outperforming traditional concatenative systems.
Survival Regression with Proper Scoring Rules and Monotonic Neural Networks
mrhuff/sumo-net
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We consider frequently used scoring rules for right-censored survival regression models such as time-dependent concordance, survival-CRPS, integrated Brier score and integrated binomial log-likelihood, and prove that neither of them is a proper scoring rule.
Beyond calibration: estimating the grouping loss of modern neural networks
aperezlebel/beyond_calibration
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Yet calibration is not enough: even a perfectly calibrated classifier with the best possible accuracy can have confidence scores that are far from the true posterior probabilities.
Generative Adversarial Nets for Robust Scatter Estimation: A Proper Scoring Rule Perspective
Robust scatter estimation is a fundamental task in statistics.
No-Regret and Incentive-Compatible Online Learning
First, we want the learning algorithm to be no-regret with respect to the best fixed expert in hindsight.
Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization
LoryPack/GenerativeNetworksScoringRulesProbabilisticForecasting
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Adversarial-free minimization is possible for some scoring rules; hence, our framework avoids the cumbersome hyperparameter tuning and uncertainty underestimation due to unstable adversarial training, thus unlocking reliable use of generative networks in probabilistic forecasting.
Local Constraint-Based Causal Discovery under Selection Bias
We consider the problem of discovering causal relations from independence constraints selection bias in addition to confounding is present.
Object Detection as Probabilistic Set Prediction
Accurate uncertainty estimates are essential for deploying deep object detectors in safety-critical systems.
