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distribution shift

985 papers

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DS

Co-occurring keywords · papers mentioning both

domain adaptation (174) domain generalization (160) test-time adaptation (78) out-of-distribution detection (74) out-of-distribution generalization (65) representation learning (48) causal inference (47) transfer learning (39) offline reinforcement learning (37) graph neural network (36)

Papers

Wasserstein-Regularized Conformal Prediction under General Distribution Shift ICLR 2025 "What is Different Between These Datasets?" A Framework for Explaining Data Distribution Shifts JMLR 2025 When Punctuation Matters: A Large-Scale Comparison of Prompt Robustness Methods for LLMs EMNLP 2025 When to retrain a machine learning model ICML 2025 Achievable distributional robustness when the robust risk is only partially identified NeurIPS 2024 A Closer Look at the Few-Shot Adaptation of Large Vision-Language Models CVPR 2024 Adapting to Distribution Shift by Visual Domain Prompt Generation ICLR 2024 Adapting to Shifting Correlations with Unlabeled Data Calibration ECCV 2024 Adaptive Labeling for Efficient Out-of-distribution Model Evaluation NeurIPS 2024 A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data NeurIPS 2024 AHA: Human-Assisted Out-of-Distribution Generalization and Detection NeurIPS 2024 An Empirical Study Into What Matters for Calibrating Vision-Language Models ICML 2024 Any-Shift Prompting for Generalization over Distributions CVPR 2024 A Survey of Learning Criteria Going beyond the Usual Risk (Abstract Reprint) AAAI 2024 A Systematic Analysis on the Temporal Generalization of Language Models in Social Media ACL 2024 ATraDiff: Accelerating Online Reinforcement Learning with Imaginary Trajectories ICML 2024 A Twist for Graph Classification: Optimizing Causal Information Flow in Graph Neural Networks AAAI 2024 AUC Maximization under Positive Distribution Shift NeurIPS 2024 Automatic De-Biased Temporal-Relational Modeling for Stock Investment Recommendation IJCAI 2024 BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning NeurIPS 2024 Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex. NeurIPS 2024 Beyond Document Page Classification: Design, Datasets, and Challenges WACV 2024 Breaking Long-Tailed Learning Bottlenecks: A Controllable Paradigm with Hypernetwork-Generated Diverse Experts NeurIPS 2024 Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift NeurIPS 2024 CaDeT: a Causal Disentanglement Approach for Robust Trajectory Prediction in Autonomous Driving CVPR 2024