{"total":17,"rows":[{"id":"2208.07590","title":"Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Risk","created":"2022-08-16","cited_by_count":19,"area":null},{"id":"2404.17652","title":"Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events","created":"2024-04-26","cited_by_count":14,"area":null},{"id":"2208.07581","title":"Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks","created":"2022-08-16","cited_by_count":11,"area":null},{"id":"2110.13413","title":"Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features","created":"2021-10-26","cited_by_count":9,"area":null},{"id":"2303.03084","title":"On Regression in Extreme Regions","created":"2023-03-06","cited_by_count":6,"area":null},{"id":"2410.23246","title":"Progression: an extrapolation principle for regression","created":"2024-10-30","cited_by_count":6,"area":null},{"id":"2505.08578","title":"Extreme Conformal Prediction: Reliable Intervals for High-Impact Events","created":"2025-05-13","cited_by_count":5,"area":null},{"id":"2407.15900","title":"Improving probabilistic forecasts of extreme wind speeds by training statistical post-processing models with weighted scoring rules","created":"2024-07-22","cited_by_count":2,"area":null},{"id":"2411.15674","title":"Quantile deep learning models for multi-step ahead time series prediction","created":"2024-11-24","cited_by_count":1,"area":null},{"id":"2412.14916","title":"From Point to probabilistic gradient boosting for claim frequency and severity prediction","created":"2024-12-19","cited_by_count":1,"area":null},{"id":"2111.00267","title":"Modelling and simulating spatial extremes by combining extreme value theory with generative adversarial networks","created":"2021-10-30","cited_by_count":0,"area":null},{"id":"2410.14062","title":"Data-driven rainfall prediction at a regional scale: a case study with Ghana","created":"2024-10-17","cited_by_count":0,"area":null},{"id":"2602.01912","title":"Reliable Real-Time Value at Risk Estimation via Quantile Regression Forest with Conformal Calibration","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2605.07752","title":"Robust and Reliable AI for Predictive Quality in Semiconductor Materials Manufacturing with MLOps and Uncertainty Quantification","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2605.27474","title":"Stop Suppressing the Tail: Causal Inference for Extreme Events","created":"2026-05-26","cited_by_count":0,"area":null},{"id":"2606.14268","title":"Gradient boosting for extremes: sampling theory and application to insurance","created":"2026-06-12","cited_by_count":0,"area":null},{"id":"2608.15290","title":"Convolution Smoothed Quantile Regression for XGBoost","created":"2026-08-15","cited_by_count":0,"area":null}],"next_cursor":null}