14 research outputs found
Agnostic Active Learning Without Constraints
We present and analyze an agnostic active learning algorithm that works
without keeping a version space. This is unlike all previous approaches where a
restricted set of candidate hypotheses is maintained throughout learning, and
only hypotheses from this set are ever returned. By avoiding this version space
approach, our algorithm sheds the computational burden and brittleness
associated with maintaining version spaces, yet still allows for substantial
improvements over supervised learning for classification
Multi-View Active Learning in the Non-Realizable Case
The sample complexity of active learning under the realizability assumption
has been well-studied. The realizability assumption, however, rarely holds in
practice. In this paper, we theoretically characterize the sample complexity of
active learning in the non-realizable case under multi-view setting. We prove
that, with unbounded Tsybakov noise, the sample complexity of multi-view active
learning can be , contrasting to
single-view setting where the polynomial improvement is the best possible
achievement. We also prove that in general multi-view setting the sample
complexity of active learning with unbounded Tsybakov noise is
, where the order of is
independent of the parameter in Tsybakov noise, contrasting to previous
polynomial bounds where the order of is related to the parameter
in Tsybakov noise.Comment: 22 pages, 1 figur
An Adaptive Strategy for Active Learning with Smooth Decision Boundary
We present the first adaptive strategy for active learning in the setting of
classification with smooth decision boundary. The problem of adaptivity (to
unknown distributional parameters) has remained opened since the seminal work
of Castro and Nowak (2007), which first established (active learning) rates for
this setting. While some recent advances on this problem establish adaptive
rates in the case of univariate data, adaptivity in the more practical setting
of multivariate data has so far remained elusive. Combining insights from
various recent works, we show that, for the multivariate case, a careful
reduction to univariate-adaptive strategies yield near-optimal rates without
prior knowledge of distributional parameters
Adaptivity to Noise Parameters in Nonparametric Active Learning
This work addresses various open questions in the theory of active learning
for nonparametric classification. Our contributions are both statistical and
algorithmic: -We establish new minimax-rates for active learning under common
\textit{noise conditions}. These rates display interesting transitions -- due
to the interaction between noise \textit{smoothness and margin} -- not present
in the passive setting. Some such transitions were previously conjectured, but
remained unconfirmed. -We present a generic algorithmic strategy for adaptivity
to unknown noise smoothness and margin; our strategy achieves optimal rates in
many general situations; furthermore, unlike in previous work, we avoid the
need for \textit{adaptive confidence sets}, resulting in strictly milder
distributional requirements
Beyond Disagreement-based Agnostic Active Learning
We study agnostic active learning, where the goal is to learn a classifier in
a pre-specified hypothesis class interactively with as few label queries as
possible, while making no assumptions on the true function generating the
labels. The main algorithms for this problem are {\em{disagreement-based active
learning}}, which has a high label requirement, and {\em{margin-based active
learning}}, which only applies to fairly restricted settings. A major challenge
is to find an algorithm which achieves better label complexity, is consistent
in an agnostic setting, and applies to general classification problems.
In this paper, we provide such an algorithm. Our solution is based on two
novel contributions -- a reduction from consistent active learning to
confidence-rated prediction with guaranteed error, and a novel confidence-rated
predictor