4,136 research outputs found
Complementary Labels Learning with Augmented Classes
Complementary Labels Learning (CLL) arises in many real-world tasks such as
private questions classification and online learning, which aims to alleviate
the annotation cost compared with standard supervised learning. Unfortunately,
most previous CLL algorithms were in a stable environment rather than an open
and dynamic scenarios, where data collected from unseen augmented classes in
the training process might emerge in the testing phase. In this paper, we
propose a novel problem setting called Complementary Labels Learning with
Augmented Classes (CLLAC), which brings the challenge that classifiers trained
by complementary labels should not only be able to classify the instances from
observed classes accurately, but also recognize the instance from the Augmented
Classes in the testing phase. Specifically, by using unlabeled data, we propose
an unbiased estimator of classification risk for CLLAC, which is guaranteed to
be provably consistent. Moreover, we provide generalization error bound for
proposed method which shows that the optimal parametric convergence rate is
achieved for estimation error. Finally, the experimental results on several
benchmark datasets verify the effectiveness of the proposed method
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback
We develop a learning principle and an efficient algorithm for batch learning
from logged bandit feedback. This learning setting is ubiquitous in online
systems (e.g., ad placement, web search, recommendation), where an algorithm
makes a prediction (e.g., ad ranking) for a given input (e.g., query) and
observes bandit feedback (e.g., user clicks on presented ads). We first address
the counterfactual nature of the learning problem through propensity scoring.
Next, we prove generalization error bounds that account for the variance of the
propensity-weighted empirical risk estimator. These constructive bounds give
rise to the Counterfactual Risk Minimization (CRM) principle. We show how CRM
can be used to derive a new learning method -- called Policy Optimizer for
Exponential Models (POEM) -- for learning stochastic linear rules for
structured output prediction. We present a decomposition of the POEM objective
that enables efficient stochastic gradient optimization. POEM is evaluated on
several multi-label classification problems showing substantially improved
robustness and generalization performance compared to the state-of-the-art.Comment: 10 page
The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective
Data augmentation (DA) is a powerful workhorse for bolstering performance in
modern machine learning. Specific augmentations like translations and scaling
in computer vision are traditionally believed to improve generalization by
generating new (artificial) data from the same distribution. However, this
traditional viewpoint does not explain the success of prevalent augmentations
in modern machine learning (e.g. randomized masking, cutout, mixup), that
greatly alter the training data distribution. In this work, we develop a new
theoretical framework to characterize the impact of a general class of DA on
underparameterized and overparameterized linear model generalization. Our
framework reveals that DA induces implicit spectral regularization through a
combination of two distinct effects: a) manipulating the relative proportion of
eigenvalues of the data covariance matrix in a training-data-dependent manner,
and b) uniformly boosting the entire spectrum of the data covariance matrix
through ridge regression. These effects, when applied to popular augmentations,
give rise to a wide variety of phenomena, including discrepancies in
generalization between over-parameterized and under-parameterized regimes and
differences between regression and classification tasks. Our framework
highlights the nuanced and sometimes surprising impacts of DA on
generalization, and serves as a testbed for novel augmentation design.Comment: 72 pages, 8 figure
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