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Poster
Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin
Ilias Diakonikolas · Daniel Kane · Pasin Manurangsi

Thu Dec 12 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #233
in Theory -- Learning Theory »
We study the problem of {\em properly} learning large margin halfspaces in the agnostic PAC model. In more detail, we study the complexity of properly learning $d$-dimensional halfspaces on the unit ball within misclassification error $\alpha \cdot \opt_{\gamma} + \eps$, where $\opt_{\gamma}$ is the optimal $\gamma$-margin error rate and $\alpha \geq 1$ is the approximation ratio. We give learning algorithms and computational hardness results for this problem, for all values of the approximation ratio $\alpha \geq 1$, that are nearly-matching for a range of parameters. Specifically, for the natural setting that $\alpha$ is any constant bigger than one, we provide an essentially tight complexity characterization. On the positive side, we give an $\alpha = 1.01$-approximate proper learner that uses $O(1/(\eps^2\gamma^2))$ samples (which is optimal) and runs in time $\poly(d/\eps) \cdot 2^{\tilde{O}(1/\gamma^2)}$. On the negative side, we show that {\em any} constant factor approximate proper learner has runtime $\poly(d/\eps) \cdot 2^{(1/\gamma)^{2-o(1)}}$, assuming the Exponential Time Hypothesis.
Keywords: Learning Theory · Hardness of Learning and Approximations · Theory
[ Paper]  [ Slides] 

Author Information

Ilias Diakonikolas (UW Madison)
Daniel Kane (UCSD)
Pasin Manurangsi (Google)

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