1 code implementation • NeurIPS 2023 • Charles Marx, Sofian Zalouk, Stefano Ermon
Calibration ensures that probabilistic forecasts meaningfully capture uncertainty by requiring that predicted probabilities align with empirical frequencies.
no code implementations • 23 Feb 2023 • Shachi Deshpande, Charles Marx, Volodymyr Kuleshov
In the context of Bayesian optimization, an online model-based decision-making task in which the data distribution shifts over time, our method yields accelerated convergence to improved optima.
no code implementations • 23 Jun 2022 • Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon
We introduce a versatile class of algorithms for recalibration in regression that we call Modular Conformal Calibration (MCC).
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.