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Bibliografiske detaljer
Main Authors:	Álvarez, Verónica, Mazuelas, Santiago, Lozano, José A.
Format:	Preprint
Udgivet:	2020
Fag:	Machine Learning
Online adgang:	https://arxiv.org/abs/2011.14721
Tags:	Tilføj Tag Ingen Tags, Vær først til at tagge denne postø!

Lignende værker

Multi-task Online Learning for Probabilistic Load Forecasting
af: Zaballa, Onintze, et al.
Udgivet: (2025)

Adaptive Multi-task Learning for Probabilistic Load Forecasting
af: Zaballa, Onintze, et al.
Udgivet: (2025)

Supervised Learning with Evolving Tasks and Performance Guarantees
af: Álvarez, Verónica, et al.
Udgivet: (2025)

Robust Minimax Boosting with Performance Guarantees
af: Mazuelas, Santiago, et al.
Udgivet: (2025)

Learnability with Partial Labels and Adaptive Nearest Neighbors
af: Errandonea, Nicolas A., et al.
Udgivet: (2026)

Reliable Programmatic Weak Supervision with Confidence Intervals for Label Probabilities
af: Álvarez, Verónica, et al.
Udgivet: (2025)

Split Conformal Classification with Unsupervised Calibration
af: Mazuelas, Santiago
Udgivet: (2025)

Safe Fairness Guarantees Without Demographics in Classification: Spectral Uncertainty Set Perspective
af: Barrainkua, Ainhize, et al.
Udgivet: (2026)

MRCpy: A Library for Minimax Risk Classifiers
af: Bondugula, Kartheek, et al.
Udgivet: (2021)

Efficient Large-Scale Learning of Minimax Risk Classifiers
af: Bondugula, Kartheek, et al.
Udgivet: (2025)

On the Optimality of the Median-of-Means Estimator under Adversarial Contamination
af: de Juan, Xabier, et al.
Udgivet: (2025)

Minimax Generalized Cross-Entropy
af: Bondugula, Kartheek, et al.
Udgivet: (2026)

External Data-Enhanced Meta-Representation for Adaptive Probabilistic Load Forecasting
af: Li, Haoran, et al.
Udgivet: (2025)

Bayesian Transformer for Probabilistic Load Forecasting in Smart Grids
af: Debnath, Sajib, et al.
Udgivet: (2026)

Stacking for Probabilistic Short-term Load Forecasting
af: Dudek, Grzegorz
Udgivet: (2024)

Adaptive Ensemble Learning with Gaussian Copula for Load Forecasting
af: Yang, Junying, et al.
Udgivet: (2025)

Social Processes: Probabilistic Meta-learning for Adaptive Multiparty Interaction Forecasting
af: Jučas, Augustinas, et al.
Udgivet: (2025)

Uncertainty-Aware Explanations Through Probabilistic Self-Explainable Neural Networks
af: Vadillo, Jon, et al.
Udgivet: (2024)

DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging Load
af: Li, Siyang, et al.
Udgivet: (2024)

Resilient Load Forecasting under Climate Change: Adaptive Conditional Neural Processes for Few-Shot Extreme Load Forecasting
af: Hu, Chenxi, et al.
Udgivet: (2026)

Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles
af: Bruninx, Max, et al.
Udgivet: (2026)

Online Conformal Probabilistic Numerics via Adaptive Edge-Cloud Offloading
af: Hou, Qiushuo, et al.
Udgivet: (2025)

Mixability of Integral Losses: a Key to Efficient Online Aggregation of Functional and Probabilistic Forecasts
af: Korotin, Alexander, et al.
Udgivet: (2019)

Effective Probabilistic Time Series Forecasting with Fourier Adaptive Noise-Separated Diffusion
af: Wang, Xinyan, et al.
Udgivet: (2025)

Probabilistic Hash Embeddings for Online Learning of Categorical Features
af: Li, Aodong, et al.
Udgivet: (2025)

M$^2$OE$^2$-GL: A Family of Probabilistic Load Forecasters That Scales to Massive Customers
af: Li, Haoran, et al.
Udgivet: (2025)

Probabilistic Constraint for Safety-Critical Reinforcement Learning
af: Chen, Weiqin, et al.
Udgivet: (2023)

Probabilistic Forecasting of Localized Wildfire Spread Based on Conditional Flow Matching
af: Shaddy, Bryan, et al.
Udgivet: (2026)

Calibrated Probabilistic Forecasts for Arbitrary Sequences
af: Marx, Charles, et al.
Udgivet: (2024)

Probabilistic Traffic Forecasting with Dynamic Regression
af: Zheng, Vincent Zhihao, et al.
Udgivet: (2023)

Automated Deep Learning for Load Forecasting
af: Keisler, Julie, et al.
Udgivet: (2024)

ProbRes: Volatility Learning for Probabilistic Time-Series Forecasting
af: Wang, Tingting, et al.
Udgivet: (2026)

Online Data Augmentation for Forecasting with Deep Learning
af: Cerqueira, Vitor, et al.
Udgivet: (2024)

Exploring Lightweight Federated Learning for Distributed Load Forecasting
af: Duttagupta, Abhishek, et al.
Udgivet: (2024)

Adaptive Conformal Inference for Multi-Step Ahead Time-Series Forecasting Online
af: Szabadváry, Johan Hallberg
Udgivet: (2024)

Simplifying Random Forests' Probabilistic Forecasts
af: Koster, Nils, et al.
Udgivet: (2024)

Probabilistic Demand Forecasting with Graph Neural Networks
af: Kozodoi, Nikita, et al.
Udgivet: (2024)

Probabilistic Forecasting via Autoregressive Flow Matching
af: ElGazzar, Ahmed, et al.
Udgivet: (2025)

Koopman Ensembles for Probabilistic Time Series Forecasting
af: Frion, Anthony, et al.
Udgivet: (2024)

Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
af: Chen, Yifan, et al.
Udgivet: (2024)