2024 Poster "distribution shifts" Papers
20 papers found
An Empirical Study Into What Matters for Calibrating Vision-Language Models
Weijie Tu, Weijian Deng, Dylan Campbell et al.
CLAP: Isolating Content from Style through Contrastive Learning with Augmented Prompts
Yichao Cai, Yuhang Liu, Zhen Zhang 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.
Efficient Diffusion-Driven Corruption Editor for Test-Time Adaptation
Yeongtak Oh, Jonghyun Lee, Jooyoung Choi 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 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 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
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
TTT-MIM: Test-Time Training with Masked Image Modeling for Denoising Distribution Shifts
Youssef Mansour, Xuyang Zhong, Serdar Caglar et al.