arXiv Machine Learning
Jun 8
MIST: Mutual Information Estimation Via Supervised Training
arXiv:2511. 18945v4 Announce Type: replace Abstract: We propose a fully data-driven approach to designing mutual information (MI) estimators.
Efficient Analytic Uncertainty Quantification for Multi-Modal Regression
arXiv:2606. 25188v1 Announce Type: new Abstract: Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning.
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arXiv Machine Learning
Aug 4
Nonparametric Distribution Regression Re-calibration
arXiv:2602. 13362v2 Announce Type: replace-cross Abstract: A key challenge in probabilistic regression is ensuring that predictive distributions accurately reflect true empirical uncertainty.
arXiv Machine Learning
Jun 26
Ribbon: Scalable Approximation and Robust Uncertainty Quantification
arXiv:2606. 27269v1 Announce Type: cross Abstract: Reliably quantifying predictive uncertainty is difficult for complex, high-dimensional, or misspecified models.
arXiv Machine Learning
Jul 7
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
arXiv:2507. 08150v4 Announce Type: replace-cross Abstract: Accurate uncertainty quantification is critical for reliable predictive modeling.
arXiv Machine Learning
Jul 16
Parallel gradient boosting for flexible estimation of conditional distributions
arXiv:2607. 13550v1 Announce Type: cross Abstract: Boosting is one of the most successful learning techniques for standard classification and regression tasks.
Hugging Face Trending Papers
Jul 15
Parallel gradient boosting for flexible estimation of conditional distributions
Boosting is one of the most successful learning techniques for standard classification and regression tasks. Its extension to multi-output prediction problems has found an increasing number of applications in recent years.
arXiv Machine Learning
Aug 11
Conditional Diffusion for Nonparametric Instrumental Variable Quantile Regression
arXiv:2608. 08204v1 Announce Type: cross Abstract: This work proposes deep nonparametric Instrumental variable quantile regression (IVQR), a two-stage estimator that combines conditional diffusion modeling with a kernel-smoothed conditional moment formulation.
arXiv Machine Learning
Aug 5
DAIF: A Data-Driven Intermediate Fusion Framework for Multimodal Supervised Learning via Approximate Message Passing
arXiv:2608. 02769v1 Announce Type: cross Abstract: Multimodal supervised learning seeks to leverage multiple heterogeneous data sources to improve predictive performance.
arXiv AI
Jul 15
Learning-based Probabilistic Load Forecasting with Post-hoc and In-model Uncertainty
arXiv:2607. 12730v1 Announce Type: cross Abstract: Smart-building load forecasters are often trained offline on dense, multivariate, high-frequency data, but deployment may provide only hourly, feature-limited inputs.
arXiv Machine Learning
Aug 11
Personalized Federated Learning via Variance-Aware Nonparametric Empirical Bayes
arXiv:2608. 09074v1 Announce Type: cross Abstract: We develop a new approach to Personalized Federated Learning across heterogeneous clients using Nonparametric Empirical Bayes (NPEB).
arXiv Machine Learning
Jul 31
Uncertainty quantification for trustworthy deep learning: Methods and measures
arXiv:2607. 28248v1 Announce Type: cross Abstract: The deployment of deep neural networks in safety-critical domains demands reliable estimates of predictive confidence, yet conventional architectures lack principled uncertainty quantification.
arXiv Machine Learning
Jul 10
Prediction-Powered Active Testing
arXiv:2607. 08347v1 Announce Type: cross Abstract: Active testing provides a label--efficient approach to risk estimation by adaptively selecting which test points should be labelled.