In this talk, we first discuss existence and uniqueness of weak solutions to general time fractional equations and give their probabilistic representation. We then talk about sharp two- sided estimates for fundamental solutions of general time fractional equations in metric measure spaces. This is a joint work with Zhen-Qing Chen(University of Washington, USA), Takashi Kumagai (RIMS, Kyoto University, Japan) and Jian Wang (Fujian Normal University, China).
The invertibility of random matrices with iid entries has been the object of intense study over the past decade, due in part to its role in proving the circular law, as well as its importance in numerical analysis (smoothed analysis). In this talk we review recent progress in our understanding of invertibility for some non-iid models: adjacency matrices of sparse random regular digraphs, and random matrices with inhomogeneous variance profile. We will also discuss estimates for the number of singular values in short intervals. Graph regularity properties play a key role in both problems. Based in part on joint works with Walid Hachem, Jamal Najim, David Renfrew, Anirban Basak and Ofer Zeitouni.
Binary, or one-bit, representations of data arise naturally in many applications, and are appealing in both hardware implementations and algorithm design. In this talk, we provide a brief background to sparsity and 1-bit measurements, and then present new results on the problem of data classification from binary data that proposes a stochastic framework with low computation and resource costs. We illustrate the utility of the proposed approach through stylized and realistic numerical experiments, provide a theoretical analysis for a simple case, and discuss future directions.
Many problems of contemporary interest in signal processing and machine learning involve highly non-convex optimization problems. While nonconvex problems are known to be intractable in general, simple local search heuristics such as (stochastic) gradient descent are often surprisingly effective at finding global optima on real or randomly generated data. In this talk I will discuss some results explaining the success of these heuristics by connecting convergence of nonconvex optimization algorithms to supremum of certain stochastic processes. I will focus on two problems.
The first problem, concerns the recovery of a structured signal from under-sampled random quadratic measurements. I will show that projected gradient descent on a natural nonconvex formulation finds globally optimal solutions with a near minimal number of samples, breaking through local sample complexity barriers that have emerged in recent literature. I will also discuss how these new mathematical developments pave the way for a new generation of data-driven phaseless imaging systems that can utilize prior information to significantly reduce acquisition time and enhance image reconstruction, enabling nano-scale imaging at unprecedented speeds and resolutions. The second problem is about learning the optimal weights of the shallowest of neural networks consisting of a single Rectified Linear Unit (ReLU). I will discuss this problem in the high-dimensional regime where the number of observations are fewer than the ReLU weights. I will show that projected gradient descent on a natural least-squares objective, when initialization at 0, converges at a linear rate to globally optimal weights with a number of samples that is optimal up to numerical constants.