The Power of Comparisons for Actively Learning Linear Classifiers

NeurIPS 2020, 2019.

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PAC-learningProbably Approximately Correctconcept classdistribution dcomparison querylog concaveMore(11+)
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Our goal would be to minimize the number of samples the learner draws before approximately learning the concept class with high probability

Abstract:

In the world of big data, large but costly to label datasets dominate many fields. Active learning, an unsupervised alternative to the standard PAC-learning model, was introduced to explore whether adaptive labeling could learn concepts with exponentially fewer labeled samples. While previous results show that active learning performs n...More

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Introduction
  • The availability of big data and the high cost of labeling has lead to a surge of interest in active learning, an adaptive, semi-supervised learning paradigm.
  • For passive RPU-learning with comparison queries, the authors will inherit the lower bound from the PAC model (Proposition 1.3).
  • The authors will show that this bound is tight up to a linear factor in dimension, and further that employing comparison queries in general shifts the RPU model from being intractable to losing only a logarithmic factor over PAC-learning in both the passive and active regimes.
Highlights
  • In recent years, the availability of big data and the high cost of labeling has lead to a surge of interest in active learning, an adaptive, semi-supervised learning paradigm
  • Our goal would be to minimize the number of samples the learner draws before approximately learning the concept class with high probability (PAC-learning)
  • Our work adopts a mixture of the approaches of Balcan et al and Kane et al We show that by leveraging comparison queries, non-homogeneous linear separators may be learned in exponentially fewer samples as long as the distribution satisfies weak concentration and anti-concentration bounds, conditions realized by, for instance, s-concave distributions
  • Similar to the passive case, for active learning we study the query complexity q(ε, δ), the minimum number of queries to learn some pair (X, C) in either the PAC-learningProbably Approximately Correct (PAC) or Reliable and Probably Useful (RPU) learning models
  • We directly extend the original algorithm of Balcan and Long to non-homogeneous linear separators via the inclusion of comparison queries, and leverage the concentration results of Balcan and Zhang to provide an inference based algorithm for learning under s-concave distributions
  • Our algorithm labels finite samples drawn from the uniform distribution over the unit ball in d-dimensions
Results
  • Random Polytope Complexity =⇒ Lower Bound: Imagine the adversary chooses a distribution such that with high probability, every point that the learner queries is of the same sign.
  • Let D be a log-concave distribution over Rd. The query complexity of Comparison-Pool-PAC learning (D, Rd, Hd) is q(ε, δ) = O d + log + log log log
  • The authors will confirm that RPU-learning linear separators with only label queries is intractable in high dimensions, but can be made efficient in both the passive and active regimes via comparison queries.
  • Label-only case, RPU-learning is lower bounded by the expected number of vertices on a random polytope drawn from the distribution D.
  • For simple distributions such as uniform over the unit ball, this gives sample complexity which is exponential in dimension, making RPU-learning impractical for any sort of high-dimensional data.
  • The authors' positive results for comparison based RPU-learning rely on weakening the concept of inference dimension to be distribution dependent.
  • This lemma shows that RPU-learning (D, X, H) with inverse super-exponential average inference dimension loses only log factors over passive or active PAC-learning.
  • Plugging this into the query complexity sets the latter term from Corollary 3.7 to 1, giving: The authors will show that by employing comparison queries the authors can improve the average inference dimension of linear separators from 2Ω(−n log(n)) to 2−Ω(n2), but first the authors will need to review a result on inference dimension from [5].
  • Plugging this result into Corollary 3.7 gives the desired guarantee on Comparison-Pool-RPU learning query complexity.
Conclusion
  • Hn ∼ Dn. for n ≥ Ω(d log2(d)) there exists an LDT using only label and comparison queries solving the point location problem with expected depth
  • To match the methodology in lower bounding Label-Pool-RPU learning, the authors will draw the classifier uniformly from hyperplanes tangent to the unit ball.
  • If there exist simple relative t-local queries with average inference dimension 2−O(nt) over some distribution D, it would imply a passive RPU-learning algorithm over D with sample complexity n(ε, δ) = O
Summary
  • The availability of big data and the high cost of labeling has lead to a surge of interest in active learning, an adaptive, semi-supervised learning paradigm.
  • For passive RPU-learning with comparison queries, the authors will inherit the lower bound from the PAC model (Proposition 1.3).
  • The authors will show that this bound is tight up to a linear factor in dimension, and further that employing comparison queries in general shifts the RPU model from being intractable to losing only a logarithmic factor over PAC-learning in both the passive and active regimes.
  • Random Polytope Complexity =⇒ Lower Bound: Imagine the adversary chooses a distribution such that with high probability, every point that the learner queries is of the same sign.
  • Let D be a log-concave distribution over Rd. The query complexity of Comparison-Pool-PAC learning (D, Rd, Hd) is q(ε, δ) = O d + log + log log log
  • The authors will confirm that RPU-learning linear separators with only label queries is intractable in high dimensions, but can be made efficient in both the passive and active regimes via comparison queries.
  • Label-only case, RPU-learning is lower bounded by the expected number of vertices on a random polytope drawn from the distribution D.
  • For simple distributions such as uniform over the unit ball, this gives sample complexity which is exponential in dimension, making RPU-learning impractical for any sort of high-dimensional data.
  • The authors' positive results for comparison based RPU-learning rely on weakening the concept of inference dimension to be distribution dependent.
  • This lemma shows that RPU-learning (D, X, H) with inverse super-exponential average inference dimension loses only log factors over passive or active PAC-learning.
  • Plugging this into the query complexity sets the latter term from Corollary 3.7 to 1, giving: The authors will show that by employing comparison queries the authors can improve the average inference dimension of linear separators from 2Ω(−n log(n)) to 2−Ω(n2), but first the authors will need to review a result on inference dimension from [5].
  • Plugging this result into Corollary 3.7 gives the desired guarantee on Comparison-Pool-RPU learning query complexity.
  • Hn ∼ Dn. for n ≥ Ω(d log2(d)) there exists an LDT using only label and comparison queries solving the point location problem with expected depth
  • To match the methodology in lower bounding Label-Pool-RPU learning, the authors will draw the classifier uniformly from hyperplanes tangent to the unit ball.
  • If there exist simple relative t-local queries with average inference dimension 2−O(nt) over some distribution D, it would imply a passive RPU-learning algorithm over D with sample complexity n(ε, δ) = O
Tables
  • Table1: Expected sample and query complexity for PAC learning (Bd, Rd, Hd)
  • Table2: Expected sample and query complexity for RPU learning (Bd, Rd, Hd)
Download tables as Excel
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