distribution shift

Excessive Invariance Causes Adversarial Vulnerability ICLR 2019 Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift NeurIPS 2019 Information-Theoretic Considerations in Batch Reinforcement Learning ICML 2019 Learning Robust Representations by Projecting Superficial Statistics Out ICLR 2019 ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models NeurIPS 2019 Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport AISTATS 2019 Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle NeurIPS 2019 To Annotate or Not? Predicting Performance Drop under Domain Shift EMNLP 2019 Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual EMNLP 2019 Algorithms and Theory for Multiple-Source Adaptation NeurIPS 2018 Detecting and Correcting for Label Shift with Black Box Predictors ICML 2018 Does Distributionally Robust Supervised Learning Give Robust Classifiers? ICML 2018 Domain Adaptation with Adversarial Training and Graph Embeddings ACL 2018 Realistic Evaluation of Deep Semi-Supervised Learning Algorithms NeurIPS 2018 The Fine Line between Linguistic Generalization and Failure in Seq2Seq-Attention Models NAACL 2018 Adversarial Adaptation of Synthetic or Stale Data ACL 2017 Joint distribution optimal transportation for domain adaptation NeurIPS 2017 Joint Geometrical and Statistical Alignment for Visual Domain Adaptation CVPR 2017 Learning Latest Classifiers without Additional Labeled Data IJCAI 2017 Multi-Instance Learning with Key Instance Shift IJCAI 2017 Classifier Adaptation at Prediction Time CVPR 2015 Continuous Target Shift Adaptation in Supervised Learning ACML 2015 Robust Classification Under Sample Selection Bias NeurIPS 2014 Robust Learning under Uncertain Test Distributions: Relating Covariate Shift to Model Misspecification ICML 2014 Model Recommendation with Virtual Probes for Egocentric Hand Detection ICCV 2013