Saved in:
| Main Authors: | Kasa, Kevin, Zhang, Zhiyu, Yang, Heng, Taylor, Graham W. |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.01416 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Sequential Harmful Shift Detection Without Labels
by: Amoukou, Salim I., et al.
Published: (2024)
by: Amoukou, Salim I., et al.
Published: (2024)
The Benefit of Being Bayesian in Online Conformal Prediction
by: Zhang, Zhiyu, et al.
Published: (2024)
by: Zhang, Zhiyu, et al.
Published: (2024)
Open Set Label Shift with Test Time Out-of-Distribution Reference
by: Ye, Changkun, et al.
Published: (2025)
by: Ye, Changkun, et al.
Published: (2025)
Mixture-Models: a one-stop Python Library for Model-based Clustering using various Mixture Models
by: Kasa, Siva Rajesh, et al.
Published: (2024)
by: Kasa, Siva Rajesh, et al.
Published: (2024)
Label Alignment Regularization for Distribution Shift
by: Imani, Ehsan, et al.
Published: (2022)
by: Imani, Ehsan, et al.
Published: (2022)
Adapting to Covariate Shift in Real-time by Encoding Trees with Motion Equations
by: Foong, Tham Yik, et al.
Published: (2024)
by: Foong, Tham Yik, et al.
Published: (2024)
AdaptNC: Adaptive Nonconformity Scores for Conformal Prediction under Distribution Shift
by: Tumu, Renukanandan, et al.
Published: (2026)
by: Tumu, Renukanandan, et al.
Published: (2026)
Adapting to Distribution Shift by Visual Domain Prompt Generation
by: Chi, Zhixiang, et al.
Published: (2024)
by: Chi, Zhixiang, et al.
Published: (2024)
Estimating Model Performance Under Covariate Shift Without Labels
by: Białek, Jakub, et al.
Published: (2024)
by: Białek, Jakub, et al.
Published: (2024)
Adapting to Online Distribution Shifts in Deep Learning: A Black-Box Approach
by: Baby, Dheeraj, et al.
Published: (2025)
by: Baby, Dheeraj, et al.
Published: (2025)
Adapting Vision-Language Models Without Labels: A Comprehensive Survey
by: Dong, Hao, et al.
Published: (2025)
by: Dong, Hao, et al.
Published: (2025)
Discounted Adaptive Online Learning: Towards Better Regularization
by: Zhang, Zhiyu, et al.
Published: (2024)
by: Zhang, Zhiyu, et al.
Published: (2024)
Semiparametric Learning from Open-Set Label Shift Data
by: Liu, Siyan, et al.
Published: (2025)
by: Liu, Siyan, et al.
Published: (2025)
LoRD: Adapting Differentiable Driving Policies to Distribution Shifts
by: Diehl, Christopher, et al.
Published: (2024)
by: Diehl, Christopher, et al.
Published: (2024)
Fast TRAC: A Parameter-Free Optimizer for Lifelong Reinforcement Learning
by: Muppidi, Aneesh, et al.
Published: (2024)
by: Muppidi, Aneesh, et al.
Published: (2024)
Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation
by: Jang, Minguk, et al.
Published: (2024)
by: Jang, Minguk, et al.
Published: (2024)
The Hidden Cost of Modeling P(X): Vulnerability to Membership Inference Attacks in Generative Text Classifiers
by: Makroo, Owais, et al.
Published: (2025)
by: Makroo, Owais, et al.
Published: (2025)
C-Adapter: Adapting Deep Classifiers for Efficient Conformal Prediction Sets
by: Liu, Kangdao, et al.
Published: (2024)
by: Liu, Kangdao, et al.
Published: (2024)
Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift
by: Chen, Mouxiang, et al.
Published: (2023)
by: Chen, Mouxiang, et al.
Published: (2023)
Theory-inspired Label Shift Adaptation via Aligned Distribution Mixture
by: Fan, Ruidong, et al.
Published: (2024)
by: Fan, Ruidong, et al.
Published: (2024)
Adapting to Shifting Correlations with Unlabeled Data Calibration
by: Nguyen, Minh, et al.
Published: (2024)
by: Nguyen, Minh, et al.
Published: (2024)
SIMSHIFT: A Benchmark for Adapting Neural Surrogates to Distribution Shifts
by: Setinek, Paul, et al.
Published: (2025)
by: Setinek, Paul, et al.
Published: (2025)
Adapting to Continuous Covariate Shift via Online Density Ratio Estimation
by: Zhang, Yu-Jie, et al.
Published: (2023)
by: Zhang, Yu-Jie, et al.
Published: (2023)
Addressing Label Shift in Distributed Learning via Entropy Regularization
by: Wu, Zhiyuan, et al.
Published: (2025)
by: Wu, Zhiyuan, et al.
Published: (2025)
Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adapt
by: Zhang, YiFan, et al.
Published: (2024)
by: Zhang, YiFan, et al.
Published: (2024)
Decision-Focused Evaluation of Worst-Case Distribution Shift
by: Ren, Kevin, et al.
Published: (2024)
by: Ren, Kevin, et al.
Published: (2024)
ASAP: Unsupervised Post-training with Label Distribution Shift Adaptive Learning Rate
by: Park, Heewon, et al.
Published: (2025)
by: Park, Heewon, et al.
Published: (2025)
Distribution Shift Is Key to Learning Invariant Prediction
by: Zheng, Hong, et al.
Published: (2026)
by: Zheng, Hong, et al.
Published: (2026)
Label Shift Estimation With Incremental Prior Update
by: Zhang, Yunrui, et al.
Published: (2026)
by: Zhang, Yunrui, et al.
Published: (2026)
Improving Adaptive Online Learning Using Refined Discretization
by: Zhang, Zhiyu, et al.
Published: (2023)
by: Zhang, Zhiyu, et al.
Published: (2023)
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
by: Eyre, Benjamin, et al.
Published: (2023)
by: Eyre, Benjamin, et al.
Published: (2023)
Adapting Large Multimodal Models to Distribution Shifts: The Role of In-Context Learning
by: Zhou, Guanglin, et al.
Published: (2024)
by: Zhou, Guanglin, et al.
Published: (2024)
Scaling Pretrained Representations Enables Label-Free Out-of-Distribution Detection Without Fine-Tuning
by: Barkley, Brett, et al.
Published: (2026)
by: Barkley, Brett, et al.
Published: (2026)
Correcting Noisy Multilabel Predictions: Modeling Label Noise through Latent Space Shifts
by: Huang, Weipeng, et al.
Published: (2025)
by: Huang, Weipeng, et al.
Published: (2025)
Eliciting Numerical Predictive Distributions of LLMs Without Autoregression
by: Piskorz, Julianna, et al.
Published: (2026)
by: Piskorz, Julianna, et al.
Published: (2026)
Detecting is Easy, Adapting is Hard: Local Expert Growth for Visual Model-Based Reinforcement Learning under Distribution Shift
by: Zhao, Haiyang
Published: (2026)
by: Zhao, Haiyang
Published: (2026)
Distribution Free Prediction Sets for Node Classification
by: Clarkson, Jase
Published: (2022)
by: Clarkson, Jase
Published: (2022)
Robust Predictive Modeling Under Unseen Data Distribution Shifts: A Methodological Commentary
by: Duan, Hanyu, et al.
Published: (2025)
by: Duan, Hanyu, et al.
Published: (2025)
GCAL: Adapting Graph Models to Evolving Domain Shifts
by: Qiao, Ziyue, et al.
Published: (2025)
by: Qiao, Ziyue, et al.
Published: (2025)
Online Distribution Shift Detection via Recency Prediction
by: Luo, Rachel, et al.
Published: (2022)
by: Luo, Rachel, et al.
Published: (2022)
Similar Items
-
Sequential Harmful Shift Detection Without Labels
by: Amoukou, Salim I., et al.
Published: (2024) -
The Benefit of Being Bayesian in Online Conformal Prediction
by: Zhang, Zhiyu, et al.
Published: (2024) -
Open Set Label Shift with Test Time Out-of-Distribution Reference
by: Ye, Changkun, et al.
Published: (2025) -
Mixture-Models: a one-stop Python Library for Model-based Clustering using various Mixture Models
by: Kasa, Siva Rajesh, et al.
Published: (2024) -
Label Alignment Regularization for Distribution Shift
by: Imani, Ehsan, et al.
Published: (2022)