The Fourier Transform of Poisson Multinomial Distributions and its Algorithmic Applications

An $(n, k)$-Poisson Multinomial Distribution (PMD) is a random variable of the form $X = \sum{i=1}ⁿ Xi$, where the $Xi$'s are independent random vectors supported on the set of standard basis vectors in $\mathbb{R}^k.$ In this paper, we obtain a refined structural understanding of PMDs by analyzing their Fourier transform. As our core structural result, we prove that the Fourier transform of PMDs is {\em approximately sparse}, i.e., roughly speaking, its $L1$-norm is small outside a small set. By building on this result, we obtain the following applications: {\bf Learning Theory.} We design the first computationally efficient learning algorithm for PMDs with respect to the total variation distance. Our algorithm learns an arbitrary $(n, k)$-PMD within variation distance $\epsilon$ using a near-optimal sample size of $\widetilde{O}k(1/\epsilon^2),$ and runs in time $\widetilde{O}k(1/\epsilon²⁾ \cdot \log n.$ Previously, no algorithm with a $\mathrm{poly}(1/\epsilon)$ runtime was known, even for $k=3.$ {\bf Game Theory.} We give the first efficient polynomial-time approximation scheme (EPTAS) for computing Nash equilibria in anonymous games. For normalized anonymous games with $n$ players and $k$ strategies, our algorithm computes a well-supported $\epsilon$-Nash equilibrium in time $n^{O(k3)} \cdot (k/\epsilon)^{{O(k3\log(k/\epsilon)/\log\log(k/\epsilon)){k-1}}.$} The best previous algorithm for this problem had running time $n^{{(f(k)/\epsilon)k},$} where $f(k) = \Omega(k^{k2})$, for any $k>2.$ {\bf Statistics.} We prove a multivariate central limit theorem (CLT) that relates an arbitrary PMD to a discretized multivariate Gaussian with the same mean and covariance, in total variation distance. Our new CLT strengthens the CLT of Valiant and Valiant by completely removing the dependence on $n$ in the error bound.