3 papers
cs.LG2025
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.LG2024
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…
cs.LG2024
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…