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Calibrated Propensity Scores for Causal Effect Estimation
Propensity scores are commonly used to balance observed covariates while estimating treatment effects.
Adversarial Calibrated Regression for Online Decision Making
Accurately estimating uncertainty is an essential component of decision-making and forecasting in machine learning.
Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies
Oftentimes, the confounders are unobserved, but we have access to large amounts of additional unstructured data (images, text) that contain valuable proxy signal about the missing confounders.
Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation
Accurate probabilistic predictions can be characterized by two properties -- calibration and sharpness.
Calibrated Uncertainty Estimation Improves Bayesian Optimization
Bayesian optimization is a sequential procedure for obtaining the global optimum of black-box functions without knowing a priori their true form.
Towards Uncertainties in Deep Learning that Are Accurate and Calibrated
Predictive uncertainties can be characterized by two properties---calibration and sharpness.
