897 citations · 2.1k across the 32 of their papers we have counts for
34 papers
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…
Oracle Analysis of Representations for Deep Open Set Detection
Risheek Garrepalli
The problem of detecting a novel class at run time is known as Open Set Detection & is important for various real-world applications like medical application, autonomous driving, e…
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…
The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods
Thomas G. Dietterich, Alexander Guyer
In many object recognition applications, the set of possible categories is an open set, and the deployed recognition system will encounter novel objects belonging to categories uns…
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…