897 citations · 2.1k across the 32 of their papers we have counts for
22 papers · 1 filter
Reinforcement Learning with Exogenous States and Rewards
George Trimponias, Thomas G. Dietterich
Exogenous state variables and rewards can slow reinforcement learning by injecting uncontrolled variation into the reward signal. This paper formalizes exogenous state variables an…
Will My Robot Achieve My Goals? Predicting the Probability that an MDP Policy Reaches a User-Specified Behavior Target
Alexander Guyer, Thomas G. Dietterich
As an autonomous system performs a task, it should maintain a calibrated estimate of the probability that it will achieve the user's goal. If that probability falls below some desi…
Conformal Prediction Intervals for Markov Decision Process Trajectories
Thomas G. Dietterich, Jesse Hostetler
Before delegating a task to an autonomous system, a human operator may want a guarantee about the behavior of the system. This paper extends previous work on conformal prediction f…
Hidden Heterogeneity: When to Choose Similarity-Based Calibration
Kiri L. Wagstaff, Thomas G. Dietterich
Trustworthy classifiers are essential to the adoption of machine learning predictions in many real-world settings. The predicted probability of possible outcomes can inform high-st…
Deep Convolution for Irregularly Sampled Temporal Point Clouds
Erich Merrill, Stefan Lee, Li Fuxin +2
We consider the problem of modeling the dynamics of continuous spatial-temporal processes represented by irregular samples through both space and time. Such processes occur in sens…
Confidence Calibration for Domain Generalization under Covariate Shift
Yunye Gong, Xiao Lin, Yi Yao +3
Existing calibration algorithms address the problem of covariate shift via unsupervised domain adaptation. However, these methods suffer from the following limitations: 1) they req…