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stat.ML2024
Adjusting Regression Models for Conditional Uncertainty Calibration
Ruijiang Gao, Mingzhang Yin, James McInerney +1
Conformal Prediction methods have finite-sample distribution-free marginal coverage guarantees. However, they generally do not offer conditional coverage guarantees, which can be i…
stat.ML2024
Variation Due to Regularization Tractably Recovers Bayesian Deep Learning
James McInerney, Nathan Kallus
Uncertainty quantification in deep learning is crucial for safe and reliable decision-making in downstream tasks. Existing methods quantify uncertainty at the last layer or other a…
cs.LG2024
Switching the Loss Reduces the Cost in Batch (Offline) Reinforcement Learning
Alex Ayoub, Kaiwen Wang, Vincent Liu +5
We propose training fitted Q-iteration with log-loss (FQI-log) for batch reinforcement learning (RL). We show that the number of samples needed to learn a near-optimal policy with…