(PDF) Inverse Decision Modeling: Learning Interpretable.. In this paper, we develop an expressive, unifying perspective on inverse decision modeling: a framework for learning parameterized representations of sequential decision.
This paper develops an expressive, unifying perspective on inverse decision modeling: a framework for learning parameterized representations of sequential decision behavior, which.
Explaining by Imitating: Understanding Decisions by Interpretable.
To satisfy these key criteria, we propose a novel model-based Bayesian method for interpretable policy learning ("Interpole") that jointly estimates an agent’s (possibly biased).
Inverse Decision Modeling: Learning Interpretable Representations.
In this paper, we develop an expressive, unifying perspective on *inverse decision modeling*: a framework for learning parameterized representations of sequential decision.
Proceedings of Machine Learning Research The Proceedings of.
Proceedings of Machine Learning Research The Proceedings of Machine.
van der Schaar Lab at ICML 2021: tutorial, 4 papers, and 4 workshops
Titles, authors and abstracts for all 4 papers are given below. Inverse Decision Modeling: Learning Interpretable Representations of Behavior Daniel Jarrett, Alihan.
danieljarrett/Inverse-Bounded-Rational-Control github.com
Inverse Bounded Rational Control, which is given as an example instance of inverse decision modeling in the paper, is implemented in files diag/main.py and adni/main.py for the decision.
Quantitative epistemology: conceiving a new human-machine.
In this paper, we develop an expressive, unifying perspective on inverse decision modeling: a framework for learning parameterized representations of sequential decision behavior. First,.
Inverse Contextual Bandits: Learning How Behavior Evolves over.
We desire an approach to policy learning that provides (1) interpretable representations of decision-making, accounts for (2) non-stationarity in behavior, as well as.
ICML 2020
“Easily-stated problems” have a clear challenge to solve and clear rules to play by; “well-defined solutions,” fall into a easily recognizable class of answers; while a “verifiable solution” is one.