Saved in:
| Main Authors: | Tonguz, Ozan K., Taschin, Federico |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.15996 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Quantifying Distribution Shift in Traffic Signal Control with Histogram-Based GEH Distance
by: Taschin, Federico, et al.
Published: (2025)
by: Taschin, Federico, et al.
Published: (2025)
The Distribution Shift Problem in Transportation Networks using Reinforcement Learning and AI
by: Taschin, Federico, et al.
Published: (2025)
by: Taschin, Federico, et al.
Published: (2025)
CFL: On the Use of Characteristic Function Loss for Domain Alignment in Machine Learning
by: Almansour, Abdullah, et al.
Published: (2025)
by: Almansour, Abdullah, et al.
Published: (2025)
Measuring Heterogeneity in Machine Learning with Distributed Energy Distance
by: Fan, Mengchen, et al.
Published: (2025)
by: Fan, Mengchen, et al.
Published: (2025)
Kolmogorov-Smirnov GAN
by: Falkiewicz, Maciej, et al.
Published: (2024)
by: Falkiewicz, Maciej, et al.
Published: (2024)
Efficient and Stable Multi-Dimensional Kolmogorov-Smirnov Distance
by: Jacobs, Peter Matthew, et al.
Published: (2025)
by: Jacobs, Peter Matthew, et al.
Published: (2025)
Shift-Invariant Attribute Scoring for Kolmogorov-Arnold Networks via Shapley Value
by: Fan, Wangxuan, et al.
Published: (2025)
by: Fan, Wangxuan, et al.
Published: (2025)
Sufficient Invariant Learning for Distribution Shift
by: Kim, Taero, et al.
Published: (2022)
by: Kim, Taero, et al.
Published: (2022)
SurvKAN: A Fully Parametric Survival Model Based on Kolmogorov-Arnold Networks
by: Mastroleo, Marina, et al.
Published: (2026)
by: Mastroleo, Marina, et al.
Published: (2026)
Addressing Label Shift in Distributed Learning via Entropy Regularization
by: Wu, Zhiyuan, et al.
Published: (2025)
by: Wu, Zhiyuan, et al.
Published: (2025)
Graph Data Augmentation with Contrastive Learning on Covariate Distribution Shift
by: Zeng, Fanlong, et al.
Published: (2025)
by: Zeng, Fanlong, et al.
Published: (2025)
Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
by: Zhang, Zeyang, et al.
Published: (2024)
by: Zhang, Zeyang, et al.
Published: (2024)
Few-shot Cross-country Generalization of Tabular Machine Learning and Foundation Models for Childhood Anemia Prediction under Distribution Shift
by: Brima, Yusuf, et al.
Published: (2026)
by: Brima, Yusuf, et al.
Published: (2026)
Kolmogorov-Arnold Network for Online Reinforcement Learning
by: Kich, Victor Augusto, et al.
Published: (2024)
by: Kich, Victor Augusto, et al.
Published: (2024)
Learning Stable Predictors from Weak Supervision under Distribution Shift
by: Shoeibi, Mehrdad, et al.
Published: (2026)
by: Shoeibi, Mehrdad, et al.
Published: (2026)
Graphs Generalization under Distribution Shifts
by: Tian, Qin, et al.
Published: (2024)
by: Tian, Qin, et al.
Published: (2024)
Utilizing Class Separation Distance for the Evaluation of Corruption Robustness of Machine Learning Classifiers
by: Siedel, Georg, et al.
Published: (2022)
by: Siedel, Georg, et al.
Published: (2022)
Low Tensor-Rank Adaptation of Kolmogorov--Arnold Networks
by: Gao, Yihang, et al.
Published: (2025)
by: Gao, Yihang, et al.
Published: (2025)
A Graph Meta-Network for Learning on Kolmogorov-Arnold Networks
by: Bar-Shalom, Guy, et al.
Published: (2026)
by: Bar-Shalom, Guy, et al.
Published: (2026)
Out-of-Distribution Detection using Counterfactual Distance
by: Stoica, Maria, et al.
Published: (2025)
by: Stoica, Maria, et al.
Published: (2025)
Weak-to-Strong Generalization under Distribution Shifts
by: Jeon, Myeongho, et al.
Published: (2025)
by: Jeon, Myeongho, et al.
Published: (2025)
Handling Distribution Shifts on Graphs: An Invariance Perspective
by: Wu, Qitian, et al.
Published: (2022)
by: Wu, Qitian, et al.
Published: (2022)
Assessing the Impact of Distribution Shift on Reinforcement Learning Performance
by: Fujimoto, Ted, et al.
Published: (2024)
by: Fujimoto, Ted, et al.
Published: (2024)
Soft Prompts for Evaluation: Measuring Conditional Distance of Capabilities
by: Nordby, Ross
Published: (2025)
by: Nordby, Ross
Published: (2025)
Learning Run-time Safety Monitors for Machine Learning Components
by: Vardal, Ozan, et al.
Published: (2024)
by: Vardal, Ozan, et al.
Published: (2024)
When Are Learning Biases Equivalent? A Unifying Framework for Fairness, Robustness, and Distribution Shift
by: Mehta, Sushant
Published: (2025)
by: Mehta, Sushant
Published: (2025)
CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous using Time Shift Governor
by: Kim, Taehyeun, et al.
Published: (2024)
by: Kim, Taehyeun, et al.
Published: (2024)
GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts
by: Zou, Deyu, et al.
Published: (2023)
by: Zou, Deyu, et al.
Published: (2023)
Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts
by: Jiang, Haiyang, et al.
Published: (2024)
by: Jiang, Haiyang, et al.
Published: (2024)
Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning
by: Arnaiz-Rodriguez, Adrian, et al.
Published: (2025)
by: Arnaiz-Rodriguez, Adrian, et al.
Published: (2025)
Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning
by: Arasteh, Fazel, et al.
Published: (2024)
by: Arasteh, Fazel, et al.
Published: (2024)
Kolmogorov-Arnold Fourier Networks
by: Zhang, Jusheng, et al.
Published: (2025)
by: Zhang, Jusheng, et al.
Published: (2025)
Clifford Kolmogorov-Arnold Networks
by: Wolff, Matthias, et al.
Published: (2026)
by: Wolff, Matthias, et al.
Published: (2026)
Graph Fairness Learning under Distribution Shifts
by: Li, Yibo, et al.
Published: (2024)
by: Li, Yibo, et al.
Published: (2024)
Detection of Disengagement from Voluntary Quizzes: An Explainable Machine Learning Approach in Higher Distance Education
by: Parsaeifard, Behnam, et al.
Published: (2025)
by: Parsaeifard, Behnam, et al.
Published: (2025)
Acceleration of Grokking in Learning Arithmetic Operations via Kolmogorov-Arnold Representation
by: Park, Yeachan, et al.
Published: (2024)
by: Park, Yeachan, et al.
Published: (2024)
A Survey on Time-Series Distance Measures
by: Paparrizos, John, et al.
Published: (2024)
by: Paparrizos, John, et al.
Published: (2024)
Dynamic Mixture of Experts Against Severe Distribution Shifts
by: Kim, Donghu
Published: (2025)
by: Kim, Donghu
Published: (2025)
Alleviating Structural Distribution Shift in Graph Anomaly Detection
by: Gao, Yuan, et al.
Published: (2024)
by: Gao, Yuan, et al.
Published: (2024)
Measuring Time-Series Dataset Similarity using Wasserstein Distance
by: Chen, Hongjie, et al.
Published: (2025)
by: Chen, Hongjie, et al.
Published: (2025)
Similar Items
-
Quantifying Distribution Shift in Traffic Signal Control with Histogram-Based GEH Distance
by: Taschin, Federico, et al.
Published: (2025) -
The Distribution Shift Problem in Transportation Networks using Reinforcement Learning and AI
by: Taschin, Federico, et al.
Published: (2025) -
CFL: On the Use of Characteristic Function Loss for Domain Alignment in Machine Learning
by: Almansour, Abdullah, et al.
Published: (2025) -
Measuring Heterogeneity in Machine Learning with Distributed Energy Distance
by: Fan, Mengchen, et al.
Published: (2025) -
Kolmogorov-Smirnov GAN
by: Falkiewicz, Maciej, et al.
Published: (2024)