distribution shift

SimPer: Simple Self-Supervised Learning of Periodic Targets ICLR 2023 SIMPLE: Specialized Model-Sample Matching for Domain Generalization ICLR 2023 SODA: Robust Training of Test-Time Data Adaptors NeurIPS 2023 SoTTA: Robust Test-Time Adaptation on Noisy Data Streams NeurIPS 2023 Sparse Mixture-of-Experts are Domain Generalizable Learners ICLR 2023 Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts NeurIPS 2023 Statistical Learning under Heterogeneous Distribution Shift ICML 2023 Strategic Distribution Shift of Interacting Agents via Coupled Gradient Flows NeurIPS 2023 Structural Re-weighting Improves Graph Domain Adaptation ICML 2023 Studying How to Efficiently and Effectively Guide Models with Explanations ICCV 2023 Surgical Fine-Tuning Improves Adaptation to Distribution Shifts ICLR 2023 SustainGym: Reinforcement Learning Environments for Sustainable Energy Systems NeurIPS 2023 Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective COLT 2023 Target-Aware Generative Augmentations for Single-Shot Adaptation ICML 2023 Taxonomy-Structured Domain Adaptation ICML 2023 TeST: Test-Time Self-Training Under Distribution Shift WACV 2023 Test-Time Robust Personalization for Federated Learning ICLR 2023 Test-Time Style Shifting: Handling Arbitrary Styles in Domain Generalization ICML 2023 Test-time Training for Matching-based Video Object Segmentation NeurIPS 2023 Text-Transport: Toward Learning Causal Effects of Natural Language EMNLP 2023 TIPI: Test Time Adaptation With Transformation Invariance CVPR 2023 Toward Robust Diagnosis: A Contour Attention Preserving Adversarial Defense for COVID-19 Detection AAAI 2023 Towards Distribution-shift Robust Text Classification of Emotional Content ACL 2023 Towards Explaining Distribution Shifts ICML 2023 Towards In-Distribution Compatible Out-of-Distribution Detection AAAI 2023