Rare-Event Prediction

Latest papers

11 papers3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

KITE: A Knowledge-Injected Temporal Encoder for Time-Series Event Forecasting
Oct 8, 2026Jonas Petersen, Pratik Jawahar, Riccardo Maggioni +6Rare-Event Prediction Time Series Forecasting
ETH Zurich · Forgis AG · University of Vienna
Steering Diffusion Models to Rare Events with Sequential Monte Carlo
Oct 6, 2026Aavash Subedi, Tim Reichelt, Christopher Williams +3Diffusion Model Sampling Importance Sampling
Department of Statistics, University of Oxford · Department of Physics, University of Oxford · Department of Statistics, University of British Columbia
Rare Event Estimation via Iterative Unalignment
Sep 21, 2026Hanming Yang, Daksh Mittal, Jing Dong +1Importance Sampling LLM Safety
Decision, Risk, and Operations Division, Columbia Business School
Reaching the Tail: Calibration Diversity Drives Conformal Coverage under Data Scarcity
Aug 21, 2026Donald AadithiyanRare-Event Prediction Conformal Prediction
Department of Computer Science and Engineering, University of Moratuwa, Moratuwa, Sri Lanka
Noise-Induced Predictability Redistribution Across Forecast Horizons of Extreme Events in Chaotic Dynamics
Aug 20, 2026Andrei Velichko, Viet-Thanh PhamRare-Event Prediction Chaotic Time-Series Prediction
Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin Ave., Petrozavodsk, 185910, Russia · Faculty of Electronics Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam
A Transferable Autologistic Model for Predicting Rare Failures in Heterogeneous Equipment
Aug 7, 2026Islam Benamirouche, Djemel Ziou, Feriel FassPredictive Maintenance Transfer Learning
Département d’informatique, Université de Sherbrooke, 2500 boulevard de l’Université, Sherbrooke, J1N 3C6, QC, Canada
PyroAdapt: Adapting Wildfire Prediction under Spatial Heterogeneity and Temporal Shift
May 12, 2026Enyi Jiang, Wu SunLearning to Rank Domain Adaptation
Siebel School of Computing and Data Science, University of Illiois at Urbana-Champaign · Computer Science, Stanford University · Department of Global Ecology, Carnegie Institution for Science
The Likelihood Ratio Wall: Structural Limits on Accurate Risk Assessment for Rare Violence
Apr 30, 2026Marco PollanenAlgorithmic Fairness Rare-Event Prediction
Trent University, Canada · Trent University, Peterborough, ON, Canada
From Stochastic Answers to Verifiable Reasoning: Interpretable Decision-Making with LLM-Generated Code
Feb 28, 2026Anirudh Jaidev Mahesh, Ben Griffin, Fuat Alican +8Rare-Event Prediction LLM Decision-Making
Georgia Institute of Technology · University of Oxford · Vela Research +1
Reasoning-Enhanced Rare-Event Prediction with Balanced Outcome Correction
Jan 23, 2026Vitaly Bulgakov, Alexander TurchinClass-Imbalanced Learning Rare-Event Prediction
Mass General Brigham, Boston, MA, USA · Profiteya LLC, Boston, MA, USA
Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data
May 30, 2025Ben Griffin, Aaron Ontoyin Yin, Diego Vidaurre +4Rule-Based Classification Rare-Event Prediction
University of Oxford, United Kingdom · Vela Research, San Francisco, United States · Aarhus University, Aarhus, Denmark +2

Oct 8, 2026J/K move · Enter open · S save

KITE: A Knowledge-Injected Temporal Encoder for Time-Series Event Forecasting

Jonas Petersen, Pratik Jawahar, Riccardo Maggioni, Gian-Alessandro Lombardi+5

ETH Zurich · Forgis AG · University of Vienna

Rare-Event Prediction Time Series Forecasting Multivariate Time Series Forecasting Self-Supervised Time Series Representation Learning

Open paper AskPDF arXiv

Forecasting rare, high-cost events in multivariate time-series systems is hard because their precursors are seldom observed, so a purely data-driven model has little to learn from. The engineers who run these systems, however, hold knowledge that precedes such events, information absent from the scarce labels. We call this a semantic prior and inject it into a self-supervised event forecaster through two switchable mechanisms, a covariate mechanism and an attention-bias mechanism, each the identity at its null, so one architecture serves every prior type with no per-dataset architecture search. A prior helps when it supplies information the observed window does not already contain: in a short context an informative prior beats no prior in-domain (pooled h-AUROC over the no-prior baseline: a periodicity clock +14%, p<0.001; a conservation residual +47% at a 0.25 label fraction), and a working prior transfers out-of-domain (+53%), substituting for feature transfer, while priors the window already recovers stay inert and the attention-bias mechanism shows no measurable gain. KITE is a switchable, attributable route to contextual conditioning, and a substrate for scaling toward cross-domain, pretrained time-series foundation world models that carry their governing structure explicitly.