Poster "distribution shift robustness" Papers
15 papers found
Conference
Beyond Greedy Exits: Improved Early Exit Decisions for Risk Control and Reliability
Divya Jyoti Bajpai, Manjesh Kumar Hanawal
NEURIPS 2025arXiv:2509.23666
1
citations
Black Sheep in the Herd: Playing with Spuriously Correlated Attributes for Vision-Language Recognition
Xinyu Tian, Shu Zou, Zhaoyuan Yang et al.
ICLR 2025arXiv:2502.15809
5
citations
Bootstrap Your Uncertainty: Adaptive Robust Classification Driven by Optimal-Transport
Jiawei Huang, Minming Li, Hu Ding
NEURIPS 2025
DaWin: Training-free Dynamic Weight Interpolation for Robust Adaptation
Changdae Oh, Yixuan Li, Kyungwoo Song et al.
ICLR 2025arXiv:2410.03782
16
citations
Enhancing Visual Prompting through Expanded Transformation Space and Overfitting Mitigation
Shohei Enomoto
NEURIPS 2025arXiv:2510.07823
Generative Classifiers Avoid Shortcut Solutions
Alexander Li, Ananya Kumar, Deepak Pathak
ICLR 2025arXiv:2512.25034
12
citations
Latent Space Factorization in LoRA
Shashi Kumar, Yacouba Kaloga, John Mitros et al.
NEURIPS 2025arXiv:2510.19640
Precise Diffusion Inversion: Towards Novel Samples and Few-Step Models
Jing Zuo, Luoping Cui, Chuang Zhu et al.
NEURIPS 2025
Sufficient Invariant Learning for Distribution Shift
Taero Kim, Subeen Park, Sungjun Lim et al.
CVPR 2025arXiv:2210.13533
3
citations
Test-Time Visual In-Context Tuning
Jiahao Xie, Alessio Tonioni, Nathalie Rauschmayr et al.
CVPR 2025arXiv:2503.21777
4
citations
TRUST: Test-Time Refinement using Uncertainty-Guided SSM Traverses
Sahar Dastani, Ali Bahri, Gustavo Vargas Hakim et al.
NEURIPS 2025arXiv:2509.22813
Visual Instruction Bottleneck Tuning
Changdae Oh, Jiatong Li, Shawn Im et al.
NEURIPS 2025arXiv:2505.13946
3
citations
Learning Divergence Fields for Shift-Robust Graph Representations
Qitian Wu, Fan Nie, Chenxiao Yang et al.
ICML 2024arXiv:2406.04963
2
citations
Robust Data-driven Prescriptiveness Optimization
Mehran Poursoltani, Erick Delage, Angelos Georghiou
ICML 2024arXiv:2306.05937
2
citations
WARM: On the Benefits of Weight Averaged Reward Models
Alexandre Rame, Nino Vieillard, Léonard Hussenot et al.
ICML 2024