96,102 research outputs found
Crowdsourced PAC Learning under Classification Noise
In this paper, we analyze PAC learnability from labels produced by
crowdsourcing. In our setting, unlabeled examples are drawn from a distribution
and labels are crowdsourced from workers who operate under classification
noise, each with their own noise parameter. We develop an end-to-end
crowdsourced PAC learning algorithm that takes unlabeled data points as input
and outputs a trained classifier. Our three-step algorithm incorporates
majority voting, pure-exploration bandits, and noisy-PAC learning. We prove
several guarantees on the number of tasks labeled by workers for PAC learning
in this setting and show that our algorithm improves upon the baseline by
reducing the total number of tasks given to workers. We demonstrate the
robustness of our algorithm by exploring its application to additional
realistic crowdsourcing settings.Comment: 14 page
The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime
We propose a novel technique for analyzing adaptive sampling called the {\em
Simulator}. Our approach differs from the existing methods by considering not
how much information could be gathered by any fixed sampling strategy, but how
difficult it is to distinguish a good sampling strategy from a bad one given
the limited amount of data collected up to any given time. This change of
perspective allows us to match the strength of both Fano and change-of-measure
techniques, without succumbing to the limitations of either method. For
concreteness, we apply our techniques to a structured multi-arm bandit problem
in the fixed-confidence pure exploration setting, where we show that the
constraints on the means imply a substantial gap between the
moderate-confidence sample complexity, and the asymptotic sample complexity as
found in the literature. We also prove the first instance-based
lower bounds for the top-k problem which incorporate the appropriate
log-factors. Moreover, our lower bounds zero-in on the number of times each
\emph{individual} arm needs to be pulled, uncovering new phenomena which are
drowned out in the aggregate sample complexity. Our new analysis inspires a
simple and near-optimal algorithm for the best-arm and top-k identification,
the first {\em practical} algorithm of its kind for the latter problem which
removes extraneous log factors, and outperforms the state-of-the-art in
experiments
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