extreme value theory

Extreme Value Monte Carlo Tree Search for Classical Planning AAAI 2026 Learning Max-Stable Representations that Extrapolate UAI 2026 TriSim: Tri-Dimensional Similarity Modeling with Extreme Value Theory for False-Negative Mitigation in Remote Sensing Image-Text Retrieval CVPR 2026 Analyzing and Modeling LLM Response Lengths with Extreme Value Theory: Anchoring Effects and Hybrid Distributions EMNLP 2025 Extreme Value Policy Optimization for Safe Reinforcement Learning ICML 2025 Provably Safeguarding a Classifier from OOD and Adversarial Samples ICLR 2025 Distributionally Robust Optimization as a Scalable Framework to Characterize Extreme Value Distributions UAI 2024 Follow-the-Perturbed-Leader with Fréchet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds COLT 2024 Spectral learning of multivariate extremes JMLR 2024 Extreme Q-Learning: MaxEnt RL without Entropy ICLR 2023 Self-Recover: Forecasting Block Maxima in Time Series from Predictors with Disparate Temporal Coverage Using Self-Supervised Learning IJCAI 2023 Why does Throwing Away Data Improve Worst-Group Error? ICML 2023 DeepGPD: A Deep Learning Approach for Modeling Geospatio-Temporal Extreme Events AAAI 2022 EV-GAN: Simulation of extreme events with ReLU neural networks JMLR 2022 Modeling extremes with d-max-decreasing neural networks UAI 2022 Robustness Verification for Contrastive Learning ICML 2022 Balanced Open Set Domain Adaptation via Centroid Alignment AAAI 2021 Bias-corrected peaks-over-threshold estimation of the CVaR UAI 2021 ExGAN: Adversarial Generation of Extreme Samples AAAI 2021 Feature Clustering for Support Identification in Extreme Regions ICML 2021 Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions ICML 2021 TellTail: Fast Scoring and Detection of Dense Subgraphs AAAI 2020 C2AE: Class Conditioned Auto-Encoder for Open-Set Recognition CVPR 2019 Cross-sectional Learning of Extremal Dependence among Financial Assets NeurIPS 2019 Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach ICLR 2018