6 papers
Defensive Boosting for Online Probabilistic Forecasting
Georgy Noarov, Aaron Roth
We study online probabilistic forecasting of binary outcomes chosen by an adaptive adversary. Given an online learning algorithm for a weak hypothesis class , we would like to e…
Optimal Deterministic Multicalibration and Omniprediction
Georgy Noarov, Aaron Roth
A model is multicalibrated on a collection of group weights if it is calibrated -- i.e. unbiased even conditional on its prediction -- not just overall, but also after reweight…
Prior-Agnostic Incentive-Compatible Exploration
Ramya Ramalingam, Osbert Bastani, Aaron Roth
In bandit settings, optimizing long-term regret metrics requires exploration, which corresponds to sometimes taking myopically sub-optimal actions. When a long-lived principal mere…
Robust Decision Making with Partially Calibrated Forecasts
Shayan Kiyani, Hamed Hassani, George Pappas +1
Calibration has emerged as a foundational goal in ``trustworthy machine learning'', in part because of its strong decision theoretic semantics. Independent of the underlying distri…
The Relationship between No-Regret Learning and Online Conformal Prediction
Ramya Ramalingam, Shayan Kiyani, Aaron Roth
Existing algorithms for online conformal prediction -- guaranteeing marginal coverage in adversarial settings -- are variants of online gradient descent (OGD), but their analyses o…
Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents
Shayan Kiyani, George Pappas, Aaron Roth +1
A fundamental question in data-driven decision making is how to quantify the uncertainty of predictions in ways that can usefully inform downstream action. This interface between p…