Efficient Certificates of Anti-Concentration Beyond Gaussians

A set of high dimensional points X <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$= \{x_{1},x_{2},\ldots,x_{n}\}\subseteq \mathbb{R}^{d}$</tex> in isotropic position is said to be <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\delta$</tex> -anti concentrated if for every direction <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$v$</tex>, the fraction of points in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$X$</tex> satisfying <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\left|\left\langle x_i, v\right\rangle\right| \leqslant \delta$</tex> is at most <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(\delta)$</tex>. Motivated by applications to list-decodable learning and clustering, three recent works [7], [44], [71] considered the problem of constructing efficient certificates of anti-concentration in the average case, when the set of points X corresponds to samples from a Gaussian distribution. Their certificates played a crucial role in several subsequent works in algorithmic robust statistics on list-decodable learning and settling the robust learnability of arbitrary Gaussian mixtures. Unlike related efficient certificates of concentration properties that are known for wide class of distri-butions [52], the aforementioned approach has been limited only to rotationally invariant distributions (and their affine transformations) with the only prominent example being Gaussian distributions. This work presents a new (and arguably the most natural) formulation for anti- concentration. Using this formulation, we give quasi-polynomial time verifiable sum-of-squares certificates of anti-concentration that hold for a wide class of non-Gaussian distributions including anti-concentrated bounded product distributions and uniform distributions over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{p}$</tex> balls (and their affine transformations). Consequently, our method upgrades and extends results in algorithmic robust statistics e.g., list-decodable learning and clustering, to such distributions. As in the case of previous works, our certificates are also obtained via relaxations in the sum-of-squares hierarchy. However, the nature of our argument differs significantly from prior works that formulate anti-concentration as the non-negativity of an explicit polynomial. Our argument constructs a canonical integer program for anti-concentration and analysis a SoS relaxation of it, independent of the intended application. The explicit polynomials appearing in prior works can be seen as specific dual certificates to this program. From a technical standpoint, unlike existing works that explicitly construct sum-of-squares certificates, our argument relies on duality and analyzes a pseudo-expectation on large subsets of the input points that take a small value in some direction. Our analysis uses the method of polynomial reweightings to reduce the problem to analyzing only analytically dense or sparse directions.