no code implementations • 1 Jun 2023 • Shachi Deshpande, Volodymyr Kuleshov
Propensity scores are commonly used to balance observed covariates while estimating treatment effects.
no code implementations • 23 Feb 2023 • Volodymyr Kuleshov, Shachi Deshpande
Accurately estimating uncertainty is an essential component of decision-making and forecasting in machine learning.
no code implementations • 18 Mar 2022 • Shachi Deshpande, Kaiwen Wang, Dhruv Sreenivas, Zheng Li, Volodymyr Kuleshov
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.
no code implementations • 14 Dec 2021 • Volodymyr Kuleshov, Shachi Deshpande
Accurate probabilistic predictions can be characterized by two properties -- calibration and sharpness.
no code implementations • 8 Dec 2021 • Shachi Deshpande, Charles Marx, Volodymyr Kuleshov
Accurate uncertainty estimates are important in sequential model-based decision-making tasks such as Bayesian optimization.
no code implementations • 29 Sep 2021 • Volodymyr Kuleshov, Shachi Deshpande
Predictive uncertainties can be characterized by two properties---calibration and sharpness.