Boosting is a widely used learning technique in machine learning for solving
classification problems. In boosting, one predicts the label of an example using an
ensemble of weak classifiers. While boosting has shown tremendous success on
many classification problems involving tabular data, it performs poorly on complex
classification tasks involving low-level features such as image classification tasks.
This drawback stems from the fact that boosting builds an additive model of weak
classifiers, each of which has very little predictive power. Often, the resulting
additive models are not powerful enough to approximate the complex decision
boundaries of real-world classification problems. In this work, we present a general
framework for boosting where, similar to traditional boosting, we aim to boost the
performance of a weak learner and transform it into a strong learner. However,
unlike traditional boosting, our framework allows for more complex forms of
aggregation of weak learners. In this work, we specifically focus on one form of
aggregation - function composition. We show that many popular greedy algorithms
for learning deep neural networks (DNNs) can be derived from our framework
using function compositions for aggregation. Moreover, we identify the drawbacks
of these greedy algorithms and propose new algorithms that fix these issues. Using
thorough empirical evaluation, we show that our learning algorithms have superior
performance over traditional additive boosting algorithms, as well as existing
greedy learning techniques for DNNs. An important feature of our algorithms is
that they come with strong theoretical guarantees.
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Lifelong Learning in Costly Feature Spaces
An important long-term goal in machine learning systems is to build learning agents that, like humans,
can learn many tasks over their lifetime, and moreover use information from these tasks to improve
their ability to do so efficiently. In this work, our goal is to provide new theoretical insights into the potential
of this paradigm. In particular, we propose a lifelong learning framework that adheres to a novel
notion of resource efficiency that is critical in many real-world domains where feature evaluations are
costly. That is, our learner aims to reuse information from previously learned related tasks to learn future
tasks in a feature-efficient manner. Furthermore, we consider novel combinatorial ways in which learning
tasks can relate. Specifically, we design lifelong learning algorithms for two structurally different
and widely used families of target functions: decision trees/lists and monomials/polynomials. We also
provide strong feature-efficiency guarantees for these algorithms; in fact, we show that in order to learn
future targets, we need only slightly more feature evaluations per training example than what is needed
to predict on an arbitrary example using those targets. We also provide algorithms with guarantees in an
agnostic model where not all the targets are related to each other. Finally, we also provide lower bounds
on the performance of a lifelong learner in these models, which are in fact tight under some conditions.
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- Award ID(s):
- 1525971
- NSF-PAR ID:
- 10057491
- Date Published:
- Journal Name:
- PMLR
- Volume:
- 76
- Page Range / eLocation ID:
- 250-287
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
