importance weighting

Understanding the role of importance weighting for deep learning ICLR 2021 Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization NeurIPS 2020 A One-step Approach to Covariate Shift Adaptation ACML 2020 Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation AISTATS 2020 f-Divergence Variational Inference NeurIPS 2020 Misspelling Detection from Noisy Product Images COLING 2020 Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison UAI 2020 Rethinking Importance Weighting for Deep Learning under Distribution Shift NeurIPS 2020 Simulator Calibration under Covariate Shift with Kernels AISTATS 2020 Covariate Shift Adaptation on Learning from Positive and Unlabeled Data AAAI 2019 Deep Gaussian Processes with Importance-Weighted Variational Inference ICML 2019 Detecting Overfitting via Adversarial Examples NeurIPS 2019 Imitation Learning from Imperfect Demonstration ICML 2019 Policy Evaluation with Latent Confounders via Optimal Balance NeurIPS 2019 Regularized Learning for Domain Adaptation under Label Shifts ICLR 2019 The Label Complexity of Active Learning from Observational Data NeurIPS 2019 What is the Effect of Importance Weighting in Deep Learning? ICML 2019 Chi-square Generative Adversarial Network ICML 2018 Importance Weighted Adversarial Nets for Partial Domain Adaptation CVPR 2018 Importance Weighted Transfer of Samples in Reinforcement Learning ICML 2018 Importance Weighting and Variational Inference NeurIPS 2018 On Unifying Deep Generative Models ICLR 2018 Robust Active Label Correction AISTATS 2018 Variational Rejection Sampling AISTATS 2018 On Gleaning Knowledge from Multiple Domains for Active Learning IJCAI 2017