"distribution shift robustness" Papers
18 papers found
Conference
Beyond Greedy Exits: Improved Early Exit Decisions for Risk Control and Reliability
Divya Jyoti Bajpai, Manjesh Kumar Hanawal
Black Sheep in the Herd: Playing with Spuriously Correlated Attributes for Vision-Language Recognition
Xinyu Tian, Shu Zou, Zhaoyuan Yang et al.
Bootstrap Your Uncertainty: Adaptive Robust Classification Driven by Optimal-Transport
Jiawei Huang, Minming Li, Hu Ding
DaWin: Training-free Dynamic Weight Interpolation for Robust Adaptation
Changdae Oh, Yixuan Li, Kyungwoo Song et al.
Enhancing Visual Prompting through Expanded Transformation Space and Overfitting Mitigation
Shohei Enomoto
Generative Classifiers Avoid Shortcut Solutions
Alexander Li, Ananya Kumar, Deepak Pathak
Latent Space Factorization in LoRA
Shashi Kumar, Yacouba Kaloga, John Mitros et al.
Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognition
Zheda Mai, Ping Zhang, Cheng-Hao Tu et al.
Precise Diffusion Inversion: Towards Novel Samples and Few-Step Models
Jing Zuo, Luoping Cui, Chuang Zhu et al.
SPACE: SPike-Aware Consistency Enhancement for Test-Time Adaptation in Spiking Neural Networks
Xinyu Luo, Kecheng Chen, Pao-Sheng Sun et al.
Sufficient Invariant Learning for Distribution Shift
Taero Kim, Subeen Park, Sungjun Lim et al.
Test-Time Visual In-Context Tuning
Jiahao Xie, Alessio Tonioni, Nathalie Rauschmayr et al.
TRUST: Test-Time Refinement using Uncertainty-Guided SSM Traverses
Sahar Dastani, Ali Bahri, Gustavo Vargas Hakim et al.
Visual Instruction Bottleneck Tuning
Changdae Oh, Jiatong Li, Shawn Im et al.
Learning Divergence Fields for Shift-Robust Graph Representations
Qitian Wu, Fan Nie, Chenxiao Yang et al.
Robust Data-driven Prescriptiveness Optimization
Mehran Poursoltani, Erick Delage, Angelos Georghiou
Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data
YongKyung Oh, Dongyoung Lim, Sungil Kim
WARM: On the Benefits of Weight Averaged Reward Models
Alexandre Rame, Nino Vieillard, Léonard Hussenot et al.