3,723 research outputs found
Block-Coordinate Frank-Wolfe Optimization for Structural SVMs
We propose a randomized block-coordinate variant of the classic Frank-Wolfe
algorithm for convex optimization with block-separable constraints. Despite its
lower iteration cost, we show that it achieves a similar convergence rate in
duality gap as the full Frank-Wolfe algorithm. We also show that, when applied
to the dual structural support vector machine (SVM) objective, this yields an
online algorithm that has the same low iteration complexity as primal
stochastic subgradient methods. However, unlike stochastic subgradient methods,
the block-coordinate Frank-Wolfe algorithm allows us to compute the optimal
step-size and yields a computable duality gap guarantee. Our experiments
indicate that this simple algorithm outperforms competing structural SVM
solvers.Comment: Appears in Proceedings of the 30th International Conference on
Machine Learning (ICML 2013). 9 pages main text + 22 pages appendix. Changes
from v3 to v4: 1) Re-organized appendix; improved & clarified duality gap
proofs; re-drew all plots; 2) Changed convention for Cf definition; 3) Added
weighted averaging experiments + convergence results; 4) Clarified main text
and relationship with appendi
Hyperparameter Importance Across Datasets
With the advent of automated machine learning, automated hyperparameter
optimization methods are by now routinely used in data mining. However, this
progress is not yet matched by equal progress on automatic analyses that yield
information beyond performance-optimizing hyperparameter settings. In this
work, we aim to answer the following two questions: Given an algorithm, what
are generally its most important hyperparameters, and what are typically good
values for these? We present methodology and a framework to answer these
questions based on meta-learning across many datasets. We apply this
methodology using the experimental meta-data available on OpenML to determine
the most important hyperparameters of support vector machines, random forests
and Adaboost, and to infer priors for all their hyperparameters. The results,
obtained fully automatically, provide a quantitative basis to focus efforts in
both manual algorithm design and in automated hyperparameter optimization. The
conducted experiments confirm that the hyperparameters selected by the proposed
method are indeed the most important ones and that the obtained priors also
lead to statistically significant improvements in hyperparameter optimization.Comment: \c{opyright} 2018. Copyright is held by the owner/author(s).
Publication rights licensed to ACM. This is the author's version of the work.
It is posted here for your personal use, not for redistribution. The
definitive Version of Record was published in Proceedings of the 24th ACM
SIGKDD International Conference on Knowledge Discovery & Data Minin
Feature Selection for Linear SVM with Provable Guarantees
We give two provably accurate feature-selection techniques for the linear
SVM. The algorithms run in deterministic and randomized time respectively. Our
algorithms can be used in an unsupervised or supervised setting. The supervised
approach is based on sampling features from support vectors. We prove that the
margin in the feature space is preserved to within -relative error of
the margin in the full feature space in the worst-case. In the unsupervised
setting, we also provide worst-case guarantees of the radius of the minimum
enclosing ball, thereby ensuring comparable generalization as in the full
feature space and resolving an open problem posed in Dasgupta et al. We present
extensive experiments on real-world datasets to support our theory and to
demonstrate that our method is competitive and often better than prior
state-of-the-art, for which there are no known provable guarantees.Comment: Appearing in Proceedings of 18th AISTATS, JMLR W&CP, vol 38, 201
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