Co-occurring keywords · papers mentioning both
Papers
A One-step Approach to Covariate Shift Adaptation
ACML 2020
Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation
AISTATS 2020
Disagreement-Regularized Imitation Learning
ICLR 2020
Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits
ICML 2020
Ensembles of Locally Independent Prediction Models
AAAI 2020
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models
NeurIPS 2020
Improving robustness against common corruptions by covariate shift adaptation
NeurIPS 2020
Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling
ICLR 2020
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions
NeurIPS 2020
Off-Policy Evaluation and Learning for External Validity under a Covariate Shift
NeurIPS 2020
Robust causal inference under covariate shift via worst-case subpopulation treatment effects
COLT 2020
Robust Correction of Sampling Bias using Cumulative Distribution Functions
NeurIPS 2020
Robust Learning with the Hilbert-Schmidt Independence Criterion
ICML 2020
Robustness to Spurious Correlations via Human Annotations
ICML 2020
Simulator Calibration under Covariate Shift with Kernels
AISTATS 2020
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
ICML 2020
Weakly-Supervised Disentanglement Without Compromises
ICML 2020
A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation
ALT 2019
Bias Reduction via End-to-End Shift Learning: Application to Citizen Science
AAAI 2019
Conformal Prediction Under Covariate Shift
NeurIPS 2019
Covariate Shift Adaptation on Learning from Positive and Unlabeled Data
AAAI 2019
Differentially Private Distributed Data Summarization under Covariate Shift
NeurIPS 2019
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
NeurIPS 2019
Low-Dimensional Density Ratio Estimation for Covariate Shift Correction
AISTATS 2019
Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift
AAAI 2019