covariate shift

Personalized Federated Learning under Mixture of Distributions ICML 2023 Statistical Learning under Heterogeneous Distribution Shift ICML 2023 Towards a Unified Analysis of Kernel-based Methods Under Covariate Shift NeurIPS 2023 Towards Consistent Video Editing with Text-to-Image Diffusion Models NeurIPS 2023 Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data AISTATS 2023 Unified Out-Of-Distribution Detection: A Model-Specific Perspective ICCV 2023 Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift NeurIPS 2023 A new similarity measure for covariate shift with applications to nonparametric regression ICML 2022 An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift AISTATS 2022 Domain Adaptation meets Individual Fairness. And they get along. NeurIPS 2022 Estimating and Explaining Model Performance When Both Covariates and Labels Shift NeurIPS 2022 Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder ICML 2022 GOOD: A Graph Out-of-Distribution Benchmark NeurIPS 2022 Grounding Aleatoric Uncertainty for Unsupervised Environment Design NeurIPS 2022 JAWS: Auditing Predictive Uncertainty Under Covariate Shift NeurIPS 2022 Off-Team Learning NeurIPS 2022 Optimal Representations for Covariate Shift ICLR 2022 Outcome Assumptions and Duality Theory for Balancing Weights AISTATS 2022 PAC Prediction Sets Under Covariate Shift ICLR 2022 Resonance in Weight Space: Covariate Shift Can Drive Divergence of SGD with Momentum ICLR 2022 Robust Imitation of a Few Demonstrations with a Backwards Model NeurIPS 2022 Self-Supervised Test-Time Adaptation on Video Data WACV 2022 Tackling covariate shift with node-based Bayesian neural networks ICML 2022 The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift NeurIPS 2022 Towards Discriminant Analysis Classifiers Using Online Active Learning via Myoelectric Interfaces AAAI 2022