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Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back
Vitaly Feldman

Wed Dec 07 08:20 AM -- 08:40 AM (PST) @ Area 3
in Optimization »
In stochastic convex optimization the goal is to minimize a convex function $F(x) \doteq \E_{f\sim D}[f(x)]$ over a convex set $\K \subset \R^d$ where $D$ is some unknown distribution and each $f(\cdot)$ in the support of $D$ is convex over $\K$. The optimization is based on i.i.d.~samples $f^1,f^2,\ldots,f^n$ from $D$. A common approach to such problems is empirical risk minimization (ERM) that optimizes $F_S(x) \doteq \frac{1}{n}\sum_{i\leq n} f^i(x)$. Here we consider the question of how many samples are necessary for ERM to succeed and the closely related question of uniform convergence of $F_S$ to $F$ over $\K$. We demonstrate that in the standard $\ell_p/\ell_q$ setting of Lipschitz-bounded functions over a $\K$ of bounded radius, ERM requires sample size that scales linearly with the dimension $d$. This nearly matches standard upper bounds and improves on $\Omega(\log d)$ dependence proved for $\ell_2/\ell_2$ setting in (Shalev-Shwartz et al. 2009). In stark contrast, these problems can be solved using dimension-independent number of samples for $\ell_2/\ell_2$ setting and $\log d$ dependence for $\ell_1/\ell_\infty$ setting using other approaches. We also demonstrate that for a more general class of range-bounded (but not Lipschitz-bounded) stochastic convex programs an even stronger gap appears already in dimension 2.
Keywords: Learning Theory · Convex Optimization
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Vitaly Feldman (Apple)

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