arXiv Machine Learning
Jun 9
Locally Adaptive Conformal Inference for Operator Models
arXiv:2507. 20975v5 Announce Type: replace-cross Abstract: Operator models are regression algorithms between Banach spaces of functions.
Manifold Constrained Conformal Prediction for Spatial Events
arXiv:2607. 10008v1 Announce Type: cross Abstract: We introduce a new conformal prediction method that constructs calibrated prediction sets over collections of spatial events, such as tropical cyclone genesis and earthquake locations.
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arXiv Machine Learning
Jun 9
Geometry-Aware Uncertainty Quantification via Conformal Prediction on Manifolds
arXiv:2602. 16015v2 Announce Type: replace Abstract: Conformal prediction gives finite-sample coverage guarantees for regression, but most standard constructions are designed for Euclidean output spaces.
arXiv Machine Learning
Jun 29
Trustworthy Predictive Distributions for Tail Events with Semiparametric Diagnostic Transport Maps
arXiv:2603. 11229v2 Announce Type: replace-cross Abstract: Machine learning forecast systems are moving beyond point predictions to full predictive distributions for future outcomes y conditional on complex inputs x.
arXiv Machine Learning
Jul 1
Accelerating Conformal Prediction via Approximate Leave-One-Out
arXiv:2606. 31915v1 Announce Type: cross Abstract: While conformal prediction provides a general framework for uncertainty quantification in predictive inference, its application is often limited by computational cost.
arXiv AI
23h ago
SPACE: Sample-cloud Predictive Adaptive Conformal Ellipsoids for Multivariate Time-Series Forecasting
arXiv:2608. 17333v1 Announce Type: cross Abstract: Modern probabilistic time-series forecasters often express uncertainty through forecast samples.
arXiv Machine Learning
Jun 10
Optimization-based Online Conformal Prediction for Multi-step Forecasting
arXiv:2508. 13362v2 Announce Type: replace Abstract: Conformal prediction (CP) is well-suited for uncertainty quantification in time series forecasting due to its distribution-free coverage guarantees.
arXiv AI
Jul 17
Native Extrapolation Awareness in Flow-Based Conditional Generation
arXiv:2602. 13061v2 Announce Type: replace-cross Abstract: The ability of Flow Matching (FM) to model complex conditional distributions has established it as the state-of-the-art for prediction tasks (e.
arXiv AI
Aug 11
Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance
arXiv:2605. 18793v2 Announce Type: replace-cross Abstract: Accurate spatiotemporal pattern analysis is critical in fields such as urban traffic, meteorology, and public health monitoring.
arXiv Machine Learning
Jun 11
Coverage Guarantees for Pseudo-Calibrated Conformal Prediction under Distribution Shift
arXiv:2602. 14913v2 Announce Type: replace Abstract: Conformal prediction (CP) offers distribution-free marginal coverage guarantees under an exchangeability assumption, but these guarantees can fail if the data distribution shifts.
arXiv Machine Learning
Aug 6
Real-time probabilistic tsunami forecasting via generative AI
arXiv:2608. 04327v1 Announce Type: new Abstract: Explicit onshore tsunami inundation forecasting can improve public risk awareness, but deterministically predicted inundation boundaries under highly uncertain conditions, such as near-field tsunamis generated by megathrust earthquakes, may falsely imply safety outside the boundaries.
arXiv Machine Learning
Jul 20
DELUGE: Towards Continental-Scale Daily Pluvial Flood Damage Prediction via Interpretable Conditioning on Foundation Model Embeddings
arXiv:2607. 16050v1 Announce Type: new Abstract: Pluvial (rainfall-driven) flooding accounts for 45% of National Flood Insurance Program (NFIP) claims in the United States and is harder to predict than its riverine and coastal counterparts, with existing approaches limited to coarse resolution, regional domains, or computationally intensive process-based models unsuitable for daily continental-scale use.
arXiv Machine Learning
Jul 1
Relational and Sequential Conformal Inference for Energy Time Series over Graphs via Foundation Models
arXiv:2606. 31804v1 Announce Type: new Abstract: Accurate energy demand forecasting is essential for the reliable operation and planning of modern sustainable energy systems.