Search Results for author:
Found 7 papers, 4 papers with code
Confidence Calibration for Systems with Cascaded Predictive Modules
Existing conformal prediction algorithms estimate prediction intervals at target confidence levels to characterize the performance of a regression model on new test samples.
IxDRL: A Novel Explainable Deep Reinforcement Learning Toolkit based on Analyses of Interestingness
However, existing systems lack the necessary mechanisms to provide humans with a holistic view of their competence, presenting an impediment to their adoption, particularly in critical applications where the decisions an agent makes can have significant consequences.
Global and Local Analysis of Interestingness for Competency-Aware Deep Reinforcement Learning
In this paper, we extend a recently-proposed framework for explainable RL that is based on analyses of "interestingness."
Outcome-Guided Counterfactuals for Reinforcement Learning Agents from a Jointly Trained Generative Latent Space
We present a novel generative method for producing unseen and plausible counterfactual examples for reinforcement learning (RL) agents based upon outcome variables that characterize agent behavior.
Confidence Calibration for Domain Generalization under Covariate Shift
Existing calibration algorithms address the problem of covariate shift via unsupervised domain adaptation.
Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and Limitations
We propose an explainable reinforcement learning (XRL) framework that analyzes an agent's history of interaction with the environment to extract interestingness elements that help explain its behavior.
