Rare Event

Rare event prediction focuses on accurately estimating the probability and characteristics of infrequent, high-impact occurrences across diverse fields. Current research emphasizes developing robust machine learning models, including deep learning architectures like neural networks and normalizing flows, and advanced sampling techniques such as importance sampling and reinforcement learning, to overcome challenges posed by imbalanced datasets and high dimensionality. These advancements are crucial for improving predictions in areas like risk assessment, predictive maintenance, and climate modeling, ultimately leading to more effective decision-making and resource allocation. The development of efficient and accurate methods for rare event prediction is a significant area of ongoing research with broad implications for various scientific disciplines and practical applications.

Papers

November 22, 2022

A Deep Reinforcement Learning Approach to Rare Event Estimation
Anthony Corso, Kyu-Young Kim, Shubh Gupta, Grace Gao, Mykel J. Kochenderfer
Adaptive Importance Deep Reinforcement Learning Approach Rare Event Value Based Reinforcement Learning Monte Carlo Sampling Proposal Distribution

September 5, 2022

Deep importance sampling using tensor trains with application to a priori and a posteriori rare event estimation
Tiangang Cui, Sergey Dolgov, Robert Scheichl
Importance Aware Importance Sampling Rare Event Tensor Train Preserving Transformation Posteriori Partition Importance Distribution

August 7, 2022

Human Perception as a Phenomenon of Quantization
Diederik Aerts, Jonito Aerts Arguëlles
Quantization Operator Human Cognition Quantum State Rare Event Human Perception

August 4, 2022

Reliability analysis of discrete-state performance functions via adaptive sequential sampling with detection of failure surfaces
Miroslav Vořechovský
Data Detection Surrogate Model Object Surface Rare Event Robust Method Reliability Analysis Failure Probability Performance Function

June 30, 2022

Pulse Shape Simulation and Discrimination using Machine-Learning Techniques
Shubham Dutta, Sayan Ghosh, Satyaki Bhattacharya, Satyajit Saha
Machine Learning Rare Event Group DIscrimination Shi Detector Pulse Signal Particle Identification Pulse Shape

June 1, 2022

The Phenomenon of Policy Churn
Tom Schaul, André Barreto, John Quan, Georg Ostrovski
Rare Event Greedy SLIM Value Based Reinforcement Learning Customer Churn Greedy Policy Implicit Exploration

April 26, 2022

An Empirical Study of the Occurrence of Heavy-Tails in Training a ReLU Gate
Sayar Karmakar, Anirbit Mukherjee
Empirical Study Deep Learning Algorithm Stochastic Way Rare Event Hypothesis Class Heavy Tail

March 8, 2022

Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon
Yiqi Zhong, Xianming Liu, Deming Zhai, Junjun Jiang, Xiangyang Ji
Adversarial Attack Adversarial Example Rare Event Quantum Shadow Physical World Attack Optical Adversarial

March 3, 2022

KamNet: An Integrated Spatiotemporal Deep Neural Network for Rare Event Search in KamLAND-Zen
A. Li, Z. Fu, L. Winslow, C. Grant, H. Song, H. Ozaki, I. Shimizu, A. Takeuchi
Rare Event K Net Spatiotemporal Deep Learning Physic Education Liquid Scintillator Detector

February 10, 2022

Case-based reasoning for rare events prediction on strategic sites
Vincent Vidal, Marie-Caroline Corbineau, Tugdual Ceillier
Human Prediction Complex Reasoning Satellite Image Satellite Imagery Rare Event Case Based Reasoning

January 27, 2022

Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrences
Thomas Guyet, Wenbin Zhang, Albert Bifet
Data Stream Rare Event Recurrent Event Sequential Pattern Sequential Pattern Mining

December 29, 2021

Feature-context driven Federated Meta-Learning for Rare Disease Prediction
Bingyang Chen, Tao Chen, Xingjie Zeng, Weishan Zhang, Qinghua Lu, Zhaoxiang Hou, Jiehan Zhou, Sumi Helal
Disease Prediction Rare Event Context Feature Rare Disease Federated Meta Learning

December 28, 2021

GANISP: a GAN-assisted Importance SPlitting Probability Estimator
Malik Hassanaly, Andrew Glaws, Ryan N. King
Generative Adversarial Network Rare Event