Distribution Shift

Test-Time Training for Speech-based Depression Detection Interspeech 2025 Theory of Agreement-on-the-Line in Linear Models and Gaussian Data AISTATS 2025 Distance-aware Calibration for Pre-trained Language Models EMNLP 2024 MaxEnt Loss: Calibrating Graph Neural Networks under Out-of-Distribution Shift (Student Abstract) AAAI 2024 Rethinking the Evaluation of Out-of-Distribution Detection: A Sorites Paradox NeurIPS 2024 Weak Distribution Detectors Lead to Stronger Generalizability of Vision-Language Prompt Tuning AAAI 2024 Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift NeurIPS 2023 FedFA: Federated Feature Augmentation ICLR 2023 Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization ICLR 2023 The Aggregation–Heterogeneity Trade-off in Federated Learning COLT 2023 Towards Stable Test-time Adaptation in Dynamic Wild World ICLR 2023 Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift NeurIPS 2022 On the Impact of Spurious Correlation for Out-of-Distribution Detection AAAI 2022 OoDHDR-Codec: Out-of-Distribution Generalization for HDR Image Compression AAAI 2022 The Missing Invariance Principle found -- the Reciprocal Twin of Invariant Risk Minimization NeurIPS 2022 Deep Stable Learning for Out-of-Distribution Generalization CVPR 2021 In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness ICLR 2021 Introspective Distillation for Robust Question Answering NeurIPS 2021