Characterizing the robustness of Bayesian adaptive experimental designs
to active learning bias
- URL: http://arxiv.org/abs/2205.13698v1
- Date: Fri, 27 May 2022 01:23:11 GMT
- Title: Characterizing the robustness of Bayesian adaptive experimental designs
to active learning bias
- Authors: Sabina J. Sloman, Daniel M. Oppenheimer, Stephen B. Broomell and Cosma
Rohilla Shalizi
- Abstract summary: We show that active learning bias can afflict Bayesian adaptive experimental design, depending on model misspecification.
We develop an information-theoretic measure of misspecification, and show that worse misspecification implies more severe active learning bias.
- Score: 3.1351527202068445
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Bayesian adaptive experimental design is a form of active learning, which
chooses samples to maximize the information they give about uncertain
parameters. Prior work has shown that other forms of active learning can suffer
from active learning bias, where unrepresentative sampling leads to
inconsistent parameter estimates. We show that active learning bias can also
afflict Bayesian adaptive experimental design, depending on model
misspecification. We develop an information-theoretic measure of
misspecification, and show that worse misspecification implies more severe
active learning bias. At the same time, model classes incorporating more
"noise" - i.e., specifying higher inherent variance in observations - suffer
less from active learning bias, because their predictive distributions are
likely to overlap more with the true distribution. Finally, we show how these
insights apply to a (simulated) preference learning experiment.
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