Allen Liu — Page 3 | Searcharxiv

arXiv2018

Efficiently Learning Mixtures of Mallows Models

Mixtures of Mallows models are a popular generative model for ranking data coming from a heterogeneous population. They have a variety of applications including social choice, recommendation systems and natural language processing. Here we give the first polynomial time algorithm for provably learning the parameters of a mixture of Mallows models with any constant number of components. Prior to our work, only the two component case had been settled. Our analysis revolves around a determinantal identity of Zagier which was proven in the context of mathematical physics, which we use to show polynomial identifiability and ultimately to construct test functions to peel off one component at a time. To complement our upper bounds, we show information-theoretic lower bounds on the sample complexity as well as lower bounds against restricted families of algorithms that make only local queries. Together, these results demonstrate various impediments to improving the dependence on the number of components. They also motivate the study of learning mixtures of Mallows models from the perspective of beyond worst-case analysis. In this direction, we show that when the scaling parameters of the Mallows models have separation, there are much faster learning algorithms.

cs.DS↗

arXiv2017

Bilinear generalized Radon transforms in the plane

Let $σ$ be arc-length measure on $S^1\subset \mathbb R^2$ and $Θ$ denote rotation by an angle $θ\in (0, π]$. Define a model bilinear generalized Radon transform, $$B_θ(f,g)(x)=\int_{S^1} f(x-y)g(x-Θy)\, dσ(y),$$ an analogue of the linear generalized Radon transforms of Guillemin and Sternberg \cite{GS} and Phong and Stein (e.g., \cite{PhSt91,St93}). Operators such as $B_θ$ are motivated by problems in geometric measure theory and combinatorics. For $θ<π$, we show that $B_θ: L^p({\Bbb R}^2) \times L^q({\Bbb R}^2) \to L^r({\Bbb R}^2)$ if $\left(\frac{1}{p},\frac{1}{q},\frac{1}{r}\right)\in Q$, the polyhedron with the vertices $(0,0,0)$, $(\frac{2}{3}, \frac{2}{3}, 1)$, $(0, \frac{2}{3}, \frac{1}{3})$, $(\frac{2}{3},0,\frac{1}{3})$, $(1,0,1)$, $(0,1,1)$ and $(\frac{1}{2},\frac{1}{2},\frac{1}{2})$, except for $\left( \frac{1}{2},\frac{1}{2},\frac{1}{2} \right)$, where we obtain a restricted strong type estimate. For the degenerate case $θ=π$, a more restrictive set of exponents holds. In the scale of normed spaces, $p,q,r \ge 1$, the type set $Q$ is sharp. Estimates for the same exponents are also proved for a class of bilinear generalized Radon transforms in $\mathbb R^2$ of the form $$ B(f,g)(x)=\int \int δ(ϕ_1(x,y)-t_1)δ(ϕ_2(x,z)-t_2) δ(ϕ_3(y,z)-t_3) f(y)g(z) ψ(y,z) \, dy\, dz, $$ where $δ$ denotes the Dirac distribution, $t_1,t_2,t_3\in\mathbb R$, $ψ$ is a smooth cut-off and the defining functions $ϕ_j$ satisfy some natural geometric assumptions.

math.CA↗