Rare-Event Prediction

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12 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Rethinking Optimization Methods for Hierarchical Feature Selection
Nov 6, 2026Xinyuan Song, Zekun Cai, Liang ZhaoFeature Selection Proximal Optimization
Emory University, Atlanta, GA, USA · The University of Tokyo, Tokyo, Japan
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

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Rethinking Optimization Methods for Hierarchical Feature Selection

Xinyuan Song, Zekun Cai, Liang Zhao

Emory University, Atlanta, GA, USA · The University of Tokyo, Tokyo, Japan

Feature Selection Proximal Optimization Structured Sparsity Rare-Event Prediction

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Rare-event forecasting often depends on interpretable conjunctions: a single keyword, location, or temporal cue may be weak, while their joint occurrence can identify a meaningful precursor. Hierarchical-Task Numerical conjunctive feature Learning (HTNL) selects Numerical Conjunctive Features (NCFs) through a structured sparsity objective. However, the resulting optimization problem is challenging, making optimization design critical to its practical performance. We give a unified diagnosis of four HTNL optimization families: original majorization-minimization (MM), variational quadratic form (VQF), alternating direction method of multipliers (ADMM), and weighted group lasso (WGL). Our analysis distinguishes three key factors that jointly determine HTNL optimization behavior. First, VQF and MM are algebraically equivalent under matched variational updates and optimize a squared-penalty formulation. Second, ADMM optimizes the unsquared objective through consensus splitting; we replace its truncated FISTA proximal solver with a scalar bisection update (ADMM-Bisect) for more accurate treatment of near-threshold blocks. Third, WGL targets the same unsquared objective, while all methods face rapidly decaying active-set admission scores. Across two synthetic datasets and 11 civil-unrest and flu-surveillance tasks, VQF achieves the highest mean AUC with the most compact non-collapsing supports. ADMM-Bisect reduces measured runtime, while calibrated WGL avoids the empty-support failures of its original scale. Synthetic results further show how proximal accuracy, pruning, and search budget affect candidate recovery, especially for longer conjunctions. Code will be made publicly available upon publication.