A Lagrangian Duality Approach to Active Learning
- URL: http://arxiv.org/abs/2202.04108v1
- Date: Tue, 8 Feb 2022 19:18:49 GMT
- Title: A Lagrangian Duality Approach to Active Learning
- Authors: Juan Elenter, Navid NaderiAlizadeh, Alejandro Ribeiro
- Abstract summary: We consider the batch active learning problem, where only a subset of the training data is labeled.
We formulate the learning problem using constrained optimization, where each constraint bounds the performance of the model on labeled samples.
We show, via numerical experiments, that our proposed approach performs similarly to or better than state-of-the-art active learning methods.
- Score: 119.36233726867992
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We consider the batch active learning problem, where only a subset of the
training data is labeled, and the goal is to query a batch of unlabeled samples
to be labeled so as to maximally improve model performance. We formulate the
learning problem using constrained optimization, where each constraint bounds
the performance of the model on labeled samples. Considering a primal-dual
approach, we optimize the primal variables, corresponding to the model
parameters, as well as the dual variables, corresponding to the constraints. As
each dual variable indicates how significantly the perturbation of the
respective constraint affects the optimal value of the objective function, we
use it as a proxy of the informativeness of the corresponding training sample.
Our approach, which we refer to as Active Learning via Lagrangian dualitY, or
ALLY, leverages this fact to select a diverse set of unlabeled samples with the
highest estimated dual variables as our query set. We show, via numerical
experiments, that our proposed approach performs similarly to or better than
state-of-the-art active learning methods in a variety of classification and
regression tasks. We also demonstrate how ALLY can be used in a generative mode
to create novel, maximally-informative samples. The implementation code for
ALLY can be found at https://github.com/juanelenter/ALLY.
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