20 research outputs found
Learning with a Drifting Target Concept
We study the problem of learning in the presence of a drifting target
concept. Specifically, we provide bounds on the error rate at a given time,
given a learner with access to a history of independent samples labeled
according to a target concept that can change on each round. One of our main
contributions is a refinement of the best previous results for polynomial-time
algorithms for the space of linear separators under a uniform distribution. We
also provide general results for an algorithm capable of adapting to a variable
rate of drift of the target concept. Some of the results also describe an
active learning variant of this setting, and provide bounds on the number of
queries for the labels of points in the sequence sufficient to obtain the
stated bounds on the error rates
Black-box Generalization of Machine Teaching
Hypothesis-pruning maximizes the hypothesis updates for active learning to
find those desired unlabeled data. An inherent assumption is that this learning
manner can derive those updates into the optimal hypothesis. However, its
convergence may not be guaranteed well if those incremental updates are
negative and disordered. In this paper, we introduce a black-box teaching
hypothesis employing a tighter slack term
to replace
the typical for pruning. Theoretically, we prove that, under the
guidance of this teaching hypothesis, the learner can converge into a tighter
generalization error and label complexity bound than those non-educated
learners who do not receive any guidance from a teacher:1) the generalization
error upper bound can be reduced from to approximately
, and 2) the label complexity upper bound can
be decreased from to
approximately . To be
strict with our assumption, self-improvement of teaching is firstly proposed
when loosely approximates . Against learning, we further
consider two teaching scenarios: teaching a white-box and black-box learner.
Experiments verify this idea and show better generalization performance than
the fundamental active learning strategies, such as IWAL, IWAL-D, etc