sampling bia

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets ICML 2025 DefVerify: Do Hate Speech Models Reflect Their Dataset’s Definition? COLING 2025 Geometry and Stability of Supervised Learning Problems JMLR 2025 Critical Gap Between Generalization Error and Empirical Error in Active Learning WACV 2024 Unbiased Active Semi-supervised Binary Classification Models IJCAI 2024 Fooling SHAP with Stealthily Biased Sampling ICLR 2023 Provable Detection of Propagating Sampling Bias in Prediction Models AAAI 2023 Sampling bias in NLU models: Impact and Mitigation Interspeech 2023 Understanding Contrastive Learning via Distributionally Robust Optimization NeurIPS 2023 Asymptotic optimality for active learning processes UAI 2022 Combating Sampling Bias: A Self-Training Method in Credit Risk Models AAAI 2022 Debiased Contrastive Learning of Unsupervised Sentence Representations ACL 2022 Unsupervised training data re-weighting for natural language understanding with local distribution approximation EMNLP 2022 A flexible model-free prediction-based framework for feature ranking JMLR 2021 Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces ICML 2021 Learning the Truth From Only One Side of the Story AISTATS 2021 On Statistical Bias In Active Learning: How and When to Fix It ICLR 2021 Why resampling outperforms reweighting for correcting sampling bias with stochastic gradients ICLR 2021 Class Prior Estimation with Biased Positives and Unlabeled Examples AAAI 2020 Faking Fairness via Stealthily Biased Sampling AAAI 2020 Investigating Sampling Bias in Abusive Language Detection EMNLP 2020 Robust Correction of Sampling Bias using Cumulative Distribution Functions NeurIPS 2020 Sampling Bias in Deep Active Classification: An Empirical Study EMNLP 2019 Adversarial Metric Learning IJCAI 2018 How biased are maximum entropy models? NeurIPS 2011