Jun 10, 2015 · We consider an "on-the-job" setting, where as inputs arrive, we use real-time crowdsourcing to resolve uncertainty where needed and output our ...
Oct 30, 2015 · Our goal is to deploy a high-accuracy system starting with zero training examples. We consider an on-the-job setting, where as inputs arrive ...
I On-the-job learning allows a system to query the crowd for labels on the uncertain parts of an input as it arrives before making a prediction.
We consider an on-the-job setting, where as inputs arrive, we use real-time crowd-sourcing to resolve uncertainty where needed and output our prediction when ...
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We cast our setting as a stochastic game based on Bayesian decision theory, which allows us to balance latency, cost, and accuracy objectives in a principled ...
We consider an “on-the-job” setting, where as inputs arrive, we use real-time crowdsourcing to resolve uncertainty where needed and output our prediction when ...
We consider an "on-the-job" setting, where as inputs arrive, we use real-time crowdsourcing to resolve uncertainty where needed and output our prediction when ...
We consider an on-the-job setting, where as inputs arrive, we use real-time crowdsourcing to resolve uncertainty where needed and output our prediction when ...
Aug 20, 2024 · Bayesian decision theory combines probability and decision-making to guide rational choices under uncertainty. It uses Bayes' theorem to ...
Aug 13, 2018 · Keenon Werling, Arun Tejasvi Chaganty, Percy Liang, Christopher D. Manning: On-the-Job Learning with Bayesian Decision Theory.