Extreme value theory enhances statistical learning extrapolation for rare events.
problem Challenges in traditional machine learning methods for extreme data.
method Asymptotic theory and statistical tools for tail behavior.
result Effective extrapolation methods for extreme quantiles and anomalies.
A new method embeds visual features into semantic space for open-set recognition.
problem Learning unseen classes in open-set recognition.
method Vocabulary-informed Extreme Value Learning (ViEVL) combining EVL and ViL.
result ViEVL embeds visual features into semantic space probabilistically, solving open-set recognition.
A novel model combines deep learning and extreme value theory for multivariate cyber risk prediction.
problem High dimensionality and heavy tails in multivariate cyber risk patterns.
method Combines deep learning for point predictions and extreme value theory for quantile predictions.
result The model provides satisfactory high quantile predictions and accurate point predictions.
Framework reconstructs missing spatio-temporal data for extreme value prediction.
problem Predicting extreme values from incomplete spatio-temporal data.
method Convolutional deep neural networks and autoencoder-like models for conditional sampling.
result Framework produces accurate reconstructions of missing data for extremal values.
EX-DRL improves extreme quantile prediction for financial risk management.
problem Inaccurate estimation of extreme quantiles in loss distributions.
method EX-DRL uses Generalized Pareto Distribution (GPD) to model the tail of the loss distribution and Quantile Regression (QR) to improve extreme quantile prediction.
result EX-DRL provides more precise estimates of extreme quantiles, improving risk metrics reliability.
The paper tackles catastrophic risk in reinforcement learning using extreme value theory.
problem Mitigating catastrophic risk in sequential decision making with limited observations.
method Developed POTPG, a policy gradient algorithm based on extreme value theory.
result POTPG outperforms common benchmarks in numerical experiments.
Spectral clustering identifies clusters of multivariate extremes.
problem Analyzing the dependence structure of multivariate extremes.
method Spectral clustering based on a random k-nearest neighbor graph. result Spectral clustering can consistently identify clusters of multivariate extremes under certain conditions.
Modeling time-varying extreme value dependence in European stock markets.
problem Non-stationary extremal dependence between European stock markets.
method Regression model for angular density of bivariate extreme value distribution.
result Evidence of increasing extremal dependence in recent years.
Improved GP model forecasts wireless demand extremes with better uncertainty quantification.
problem Forecasting extreme wireless demand spikes and troughs for network optimization.
method Designed a feature embedding kernel for Gaussian Process models.
result 32% reduction in short-term extreme value prediction error vs. S-ARIMA.
New classifiers tackle unknown classes with extreme value theory.
problem Classifiers struggle with unknown classes having different geometries.
method Proposes two new classifiers based on extreme value theory approximations.
result New classifiers outperform existing methods in simulations and real datasets.
A new method for choosing thresholds in data sequences without assuming distribution.
problem Choosing thresholds for random sequences without distributional assumptions.
method Data-driven threshold machine (DTM) that estimates three parameters of extreme value distributions and extremal index.
result DTM provides a reliable estimate of thresholds with robustness and computational efficiency.
Analyzes premium data of Indian non-life insurers, finding GEV distribution best fits Lognormal and GEV extremes.
problem Modeling premiums of non-life insurance companies in India.
method Empirical analysis using Lognormal, GEV, and GPD distributions.
result Generalized Extreme Value distribution best fits premium data for ten Indian non-life insurers.
Generative models learn to capture target distribution support with extreme value loss.
problem Mode collapse in generative models for non-trivial target distributions.
method Optimizing against the minimal value of the loss function, rather than the mean.
result Models trained with extreme value loss learn to capture the support of the target distribution.
Combines GANs and EVT for better modeling of spatial climate extremes.
problem Modeling dependencies between climate extremes, especially in high-dimensional spaces.
method Generative Adversarial Networks (GANs) combined with Extreme Value Theory (EVT).
result evtGAN outperforms classical GANs and statistical approaches in modeling spatial extremes.
WEINCE improves contrastive learning by correcting softmax biases.
problem Softmax in InfoNCE can lead to misaligned statistical assumptions in contrastive learning.
method WEINCE uses anchor-wise online batch statistics to blend softmax logits with an endpoint shortfall correction.
result WEINCE yields consistent improvements in frozen-feature evaluation across five vision benchmarks.
New framework assesses extreme errors in machine learning models.
problem Current validation methods fail to quantify extreme errors in high-stakes domains.
method Uses Extreme Value Theory (EVT) to estimate worst-case failures.
result Establishes EVT as a fundamental tool for assessing model reliability.
This paper uses VAE to generate extreme events from multivariate data.
problem Generating accurate extremes from observational data for risk assessment.
method Variational Autoencoder (VAE) approach for multivariate heavy-tailed distributions.
result Improves learning of dependency structure between extremes.
Extends extreme value mixture models to identify changepoints in financial extreme regimes.
problem Inference over financial extreme regimes is affected by threshold choice.
method Extends extreme value mixture models to account for distributional extreme changepoints using MCMC algorithms.
result Inclusion of different extreme regimes improves financial applications compared to static and dynamic approaches.
Novel SVM approach for extreme quantile regression with heavy tailed inputs.
problem Learning from extreme values in quantile regression.
method Support Vector Machine framework for handling high-dimensional and nonlinear settings.
result Established finite-sample learning guarantees under mild regularity assumptions.
New bandit algorithms focus on extreme values, outperforming existing methods.
problem Optimizing decisions based on extreme values rather than expected values.
method Robust statistics-based algorithms with vanishing extremal regret.
result The proposed algorithms achieve superior performance compared to existing methods.
Paper improves risk estimation for rare events in sequential decisions.
problem Estimating tail risk in high confidence levels with limited data.
method Combines extreme value theory and automated threshold selection.
result Improves performance in estimating tail risk for some distributions.
The thesis evaluates and compares extreme mixture models in finance and insurance.
problem Estimating tail risk measures in finance and insurance.
method Extreme mixture models and methods, including kernel density estimation and GARCH preprocessing.
result Kernel density estimation-based models do not outperform others in tail risk estimation.
Study extreme-case Value-at-Risk under IFR distributions, providing guidance for risk management.
problem Understanding extreme-case risk measures under distributional ambiguity and increasing failure rate.
method Characterized extreme-case range Value-at-Risk under mean and variance constraints with increasing failure rate.
result Characterized specific characteristics of extreme-case distributions under IFR constraints.
Paper uses LSTM for anomaly detection in transportation networks.
problem Anomaly detection in transportation networks.
method LSTM model combined with statistical techniques (Gaussian, EVT, Tukey's method).
result EVT-based detection rule outperforms other methods.
New algorithm ranks anomalies in high dimensions using EVT.
problem Anomaly detection in high-dimensional data.
method Multivariate Extreme Value Theory (EVT) for sparse representation.
result Sparse representation of multivariate extremes for anomaly ranking.
The paper tackles extreme value statistics for censored data with heavy tails under competing risks.
problem Estimating extreme value index and quantiles of sub-distribution function in heavy-tailed data with censoring and competing risks.
method Asymptotic normality of a novel Aalen-Johansen integral estimator is established for the extreme value index. Estimation of extreme quantiles of cumulative incidence function is also addressed.
result Asymptotic normality of the proposed estimator for extreme value index is established.
We win EVA2025 by estimating extreme precipitation events using Peaks Over Thresholds and martingale testing.
problem Estimating the probability of extreme precipitation events with limited data.
method Modeling Peaks Over Thresholds with an exponential distribution and using martingale testing for evaluation.
result Our method outperforms other approaches in estimating extreme precipitation events.
Deep learning predicts energy loads and prices with LSTM and EVT.
problem Predicting extreme loads in energy grids due to supply and demand fluctuations.
method Deep spatio-temporal models and EVT for tail behavior of load spikes.
result Deep LSTM models outperform traditional methods in capturing nonlinearities.
New method uses extreme value theory to estimate neural network errors.
problem Quantifying the error of neural networks, especially for large values.
method Applying extreme value theory to approximate the distribution of error.
result Developed a new estimator for the shape parameter of the Pareto distribution.
Extremely accurate prediction of dynamical system bifurcations using control inputs.
problem Predicting complex bifurcation structures in dynamical systems.
method Extending extreme learning machines with control inputs to model system dynamics.
result The model can nearly reproduce the entire structure of bifurcations using only a few parameter values.
Survey of extreme value modeling techniques for insurance.
problem Modeling of insurance industry's extreme events.
method Truncation, tempering, censoring, regression techniques.
result Adapted techniques for insurance applications.
New framework estimates treatment effects in extreme data.
problem Hindered by unavailability of counterfactual outcomes and rarity of extreme data.
method Proposes a new framework based on extreme value theory.
result Quantifies treatment effects using tail decay rates of potential outcomes.
Paper develops deep learning for metocean variable extremes.
problem Estimating multivariate joint extremes of metocean variables.
method SPAR model with GP distribution for radial tail, kernel density for angular variable, deep neural networks for GP parameters.
result The method provides good description of metocean variables joint extremes.
Study efficient resource allocation for detecting extreme values.
problem Efficiently allocate limited resources to detect extreme values in various fields.
method Proposes ExtremeHunter algorithm for sequential resource allocation under limited feedback.
result Demonstrates ExtremeHunter outperforms oracle policy in detecting extreme values.
Proposes a network-based strategy to manage financial market risks.
problem Managing extreme events in volatile financial markets.
method Extreme value theory, network model, maximum independent set, value at risk, expected shortfall.
result Developed portfolio strategies improve risk diversification.
GARCH-UGH improves VaR estimation for financial risk management.
problem Dynamic estimation of extreme VaR in financial time series.
method AR-GARCH filtering followed by a bias-reduced extreme value estimator.
result GARCH-UGH estimates are more accurate than conventional methods.
New method uses neural networks to predict extreme wildfires, improving accuracy over traditional models.
problem Predicting extreme wildfires using complex, non-linear relationships.
method Partially-interpretable neural networks for extreme quantile regression.
result Significant improvement in predictive performance over traditional methods.
We develop a framework for analyzing extreme values in correlated financial data.
problem Quantifying and mitigating risk in complex financial systems.
method Developed a practical framework for handling finite, multivariate, and correlated time series in finance.
result We successfully analyze high-frequency stock returns using univariate extreme value tools.
PCA simplifies multivariate extreme data analysis.
problem Analyzing multivariate extreme values with high-dimensional data.
method Principal Component Analysis (PCA) for dimensionality reduction.
result PCA helps preserve essential information for extreme value analysis.
Paper improves risk estimation for extreme events.
problem Estimating extreme risks accurately.
method Modified Bayes risk for expectiles, asymptotic expansions, efficient estimators.
result Asymptotic normality of estimators proved.
Study extreme values of stable random fields on geometric spaces.
problem Understanding extreme values of stable random fields on various geometric spaces.
method Analyzing extreme values through Patterson-Sullivan measures and extremal cocycle growth.
result Established a dichotomy for the growth-rate of maxima sequences of stable random fields.
Paper develops a novel approach to identify clusters of features in multivariate extremes.
problem Understanding the complex structure of multivariate extremes in various fields.
method Optimization-based approach to assess the dependence structure of extremes.
result Estimating clusters of features that best capture the support of extremes.
Quantum algorithm finds extremal values without direct function access.
problem Finding extremal values of hidden functions without direct access.
method Parametric quantum circuit trained with a trainable quantum feature map.
result Algorithm successfully finds extremal values even with sparse training data.
We present an alternative to the pseudo-inverse method for determining the hidden to output weight values for Extreme Learning Machines performing classification tasks. The method is based on linear discriminant analysis and provides Bayes optimal single point estimates for the weight values.
Proposes a method to model financial returns with extreme shocks using flexible tail transformations.
problem Capturing extreme shocks in financial return data.
method Introduces a transformation layer in normalizing flows to model heavy-tailed distributions.
result Trained models can generate synthetic sets of extreme returns.
Paper refines ExtremeHunter algorithm for detecting extreme values.
problem Max K-armed bandit problem, focusing on extreme value detection.
method Significantly refines ExtremeHunter algorithm and proposes an alternative approach.
result Extreme Bandits can be reduced to a classical bandit problem.
Paper studies SERA's effectiveness in optimizing imbalanced regression models.
problem Imbalanced regression tasks where extreme values are crucial.
method Gradient boosting algorithms tested with 36 datasets.
result Models using SERA as objective function perform better at extreme value predictions.
This paper uses ML and EVT to analyze tree ring data, improving accuracy of predictions.
problem Analyzing tree ring data for climate modeling and historical studies.
method Combines machine learning algorithms with extreme value theory for data analysis.
result Random Forest method yields the most accurate results for tree ring data analysis.