"distribution shifts" Papers
26 papers found
Matcha: Mitigating Graph Structure Shifts with Test-Time Adaptation
Wenxuan Bao, Zhichen Zeng, Zhining Liu et al.
RA-TTA: Retrieval-Augmented Test-Time Adaptation for Vision-Language Models
Youngjun Lee, Doyoung Kim, Junhyeok Kang et al.
Rethinking Fair Representation Learning for Performance-Sensitive Tasks
Charles Jones, Fabio De Sousa Ribeiro, Mélanie Roschewitz et al.
Uncertainty-Informed Meta Pseudo Labeling for Surrogate Modeling with Limited Labeled Data
Xingyu Ren, Pengwei Liu, Pengkai Wang et al.
Universal generalization guarantees for Wasserstein distributionally robust models
Tam Le, Jerome Malick
An Empirical Study Into What Matters for Calibrating Vision-Language Models
Weijie Tu, Weijian Deng, Dylan Campbell et al.
COALA: A Practical and Vision-Centric Federated Learning Platform
Weiming Zhuang, Jian Xu, Chen Chen et al.
Density-Softmax: Efficient Test-time Model for Uncertainty Estimation and Robustness under Distribution Shifts
Ha Manh Bui, Anqi Liu
Disentangled Graph Self-supervised Learning for Out-of-Distribution Generalization
Haoyang Li, Xin Wang, Zeyang Zhang et al.
Feature Contamination: Neural Networks Learn Uncorrelated Features and Fail to Generalize
Tianren Zhang, Chujie Zhao, Guanyu Chen et al.
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
Yongxin Guo, Xiaoying Tang, Tao Lin
Few-shot Adaptation to Distribution Shifts By Mixing Source and Target Embeddings
Yihao Xue, Ali Payani, Yu Yang et al.
Graph Invariant Learning with Subgraph Co-mixup for Out-of-Distribution Generalization
Tianrui Jia, Haoyang Li, Cheng Yang et al.
Graph Structure Extrapolation for Out-of-Distribution Generalization
Xiner Li, Shurui Gui, Youzhi Luo et al.
How Do Nonlinear Transformers Learn and Generalize in In-Context Learning?
Hongkang Li, Meng Wang, Songtao Lu et al.
IW-GAE: Importance weighted group accuracy estimation for improved calibration and model selection in unsupervised domain adaptation
Taejong Joo, Diego Klabjan
Learning by Erasing: Conditional Entropy Based Transferable Out-of-Distribution Detection
Meng Xing, Zhiyong Feng, Yong Su et al.
Learning to Intervene on Concept Bottlenecks
David Steinmann, Wolfgang Stammer, Felix Friedrich et al.
Measuring Stochastic Data Complexity with Boltzmann Influence Functions
Nathan Ng, Roger Grosse, Marzyeh Ghassemi
Multiply Robust Estimation for Local Distribution Shifts with Multiple Domains
Steven Wilkins-Reeves, Xu Chen, Qi Ma et al.
Online Adaptive Anomaly Thresholding with Confidence Sequences
Sophia Sun, Abishek Sankararaman, Balakrishnan Narayanaswamy
RetroOOD: Understanding Out-of-Distribution Generalization in Retrosynthesis Prediction
Yemin Yu, Luotian Yuan, Ying WEI et al.
Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup
Damien Teney, Jindong Wang, Ehsan Abbasnejad
Statistical Inference Under Constrained Selection Bias
Santiago Cortes-Gomez, Mateo Dulce Rubio, Carlos Miguel Patiño et al.
Statistical Properties of Robust Satisficing
zhiyi li, Yunbei Xu, Ruohan Zhan
Test-Time Degradation Adaptation for Open-Set Image Restoration
Yuanbiao Gou, Haiyu Zhao, Boyun Li et al.