3 papers
cs.LG2024
Calibrated Probabilistic Forecasts for Arbitrary Sequences
Charles Marx, Volodymyr Kuleshov, Stefano Ermon
Real-world data streams can change unpredictably due to distribution shifts, feedback loops and adversarial actors, which challenges the validity of forecasts. We present a forecas…
cs.LG2023
Calibrated Regression Against An Adversary Without Regret
Shachi Deshpande, Charles Marx, Volodymyr Kuleshov
We are interested in probabilistic prediction in online settings in which data does not follow a probability distribution. Our work seeks to achieve two goals: (1) producing valid…
cs.LG2021
Online Calibrated and Conformal Prediction Improves Bayesian Optimization
Shachi Deshpande, Charles Marx, Volodymyr Kuleshov
Accurate uncertainty estimates are important in sequential model-based decision-making tasks such as Bayesian optimization. However, these estimates can be imperfect if the data vi…