arXiv research

Researchers solve an inverse problem for a semilinear elliptic equation on complex manifolds.

Sharp bounds on quasimode norms on compact space forms.

Estimates for eigenfunctions and quasimodes on compact manifolds.

We consider Calderon's inverse problem with partial data in dimensions $n \geq 3$. If the inaccessible part of the boundary satisfies a (conformal) flatness condition in one direction, we show that this problem reduces to the invertibility of a broken geodesic ray transform. In Euclidean space, sets satisfying the flat…

Optimal estimates for spectral projection norms on compact manifolds.

We consider the anisotropic Calderon problem of recovering a conductivity matrix or a Riemannian metric from electrical boundary measurements in three and higher dimensions. In the earlier work \cite{DKSaU}, it was shown that a metric in a fixed conformal class is uniquely determined by boundary measurements under two …

Accurate asymptotic expressions are given for the exponentially small eigenvalues of Witten Laplacians acting on p-forms. The key ingredient, which replaces explicit formulas for global quasimodes in the case p = 0, is Barannikov's presentation of Morse theory.

Analyzes tunneling effects for Schrödinger operators on vector bundles.

For a symplectic manifold with quantizing line bundle, a choice of almost complex structure determines a Laplacian acting on tensor powers of the bundle. For high tensor powers Guillemin-Uribe showed that there is a well-defined cluster of low-lying eigenvalues, whose distribution is described by a spectral density fun…

In the limit $\hbar\to 0$, we analyze a class of Schrödinger operators $H_\hbar = \hbar^2 L + \hbar W + V\cdot \mathrm{id}$ acting on sections of a vector bundle $\mathcal{Eh}$ over a Riemannian manifold $M$ where $L$ is a Laplace type operator, $W$ is an endomorphism field and the potential energy $V$ has a non-degene…

If $(M,g)$ is a compact real analytic Riemannian manifold, we give a necessary and sufficient condition for there to be a sequence of quasimodes of order $o(λ)$ saturating sup-norm estimates. In particular, it gives optimal conditions for existence of eigenfunctions satisfying maximal sup norm bounds. The condition is …

Proves existence and uniqueness of solutions to the Lp Gaussian Minkowski problem.

Solves $L_p$-Gaussian chord Minkowski problem using Gauss curvature flow.

New method approximates Gaussian curvature on discrete surfaces.

Improved estimator for least squares using random projections achieves smaller error.

Study shows that ridgeless Gaussian kernel regression overfits even with varying bandwidth or dimensionality.

Decentralized Gaussian processes for multi-agent systems.

This paper proposes a method to approximate non-Gaussian likelihoods in Gaussian Processes.

One goal in Bayesian machine learning is to encode prior knowledge into prior distributions, to model data efficiently. We consider prior knowledge from systems of linear partial differential equations together with their boundary conditions. We construct multi-output Gaussian process priors with realizations in the so…

We explore the algebraic structure of the solution space of convex optimization problem Constrained Minimum Trace Factor Analysis (CMTFA), when the population covariance matrix $Σ_x$ has an additional latent graphical constraint, namely, a latent star topology. In particular, we have shown that CMTFA can have either a …

Efficiently predicts high-fidelity PDE solutions using multi-fidelity Gaussian processes.

We prove Gaussian type bounds for the fundamental solution of the conjugate heat equation evolving under the Ricci flow. As a consequence, for dimension 4 and higher, we show that the backward limit of type I $κ$-solutions of the Ricci flow must be a non-flat gradient shrinking Ricci soliton. This extends Perelman's pr…

New sampling methods improve statistical efficiency for intractable targets.

A new method uses Gaussian Processes to solve power flow problems with uncertain renewable and load inputs.

A new optimization algorithm for Gaussian Variational Inference on precision matrices.

The paper constructs instanton complexes on stratified pseudomanifolds.

We study a basic private estimation problem: each of $n$ users draws a single i.i.d. sample from an unknown Gaussian distribution, and the goal is to estimate the mean of this Gaussian distribution while satisfying local differential privacy for each user. Informally, local differential privacy requires that each data …

We study stable smooth solutions to the isoperimetric type problem for a Gaussian weight on Euclidean Space. That is, we study hypersurfaces $Σ^n \subset \mathbb R^{n+1}$ that are second order stable critical points of compact variations that minimize Gaussian weighted area and preserve Gaussian weighted volume. We sho…

We investigate the geometric characteristics of constant gaussian curvature surfaces obtained from solutions of the $G(m,n)$ sigma model. Most of these solutions are related to the Veronese sequence. We show that we can distinguish surfaces with the same gaussian curvature using additional quantities like the topologic…

Study proves only origin-centered spheres solve certain curvature problems.

It is shown that $3$ disjoint sets with fixed Gaussian volumes that partition $\mathbb{R}^{n}$ with nearly minimum total Gaussian surface area must be close to adjacent $120$ degree sectors, when $n\geq2$. These same results hold for any number $m\leq n+1$ of sets partitioning $\mathbb{R}^{n}$, conditional on the solut…

We study active learning (AL) based on Gaussian Processes (GPs) for efficiently enumerating all of the local minimum solutions of a black-box function. This problem is challenging due to the fact that local solutions are characterized by their zero gradient and positive-definite Hessian properties, but those derivative…

Proposes variational Gaussian approximations for solving the Kushner equation.

Given a Gaussian Markov random field, we consider the problem of selecting a subset of variables to observe which minimizes the total expected squared prediction error of the unobserved variables. We first show that finding an exact solution is NP-hard even for a restricted class of Gaussian Markov random fields, calle…

This paper reviews recent advances in Gaussian process regression methods.

Fractional porous media equations yield q-Gaussian solutions for stock price returns.

Paper proves rigidity for self-similar solutions in 3D flows.

The paper solves a problem in metric geometry for disks with negative curvature.

We introduce the concept of numerical Gaussian processes, which we define as Gaussian processes with covariance functions resulting from temporal discretization of time-dependent partial differential equations. Numerical Gaussian processes, by construction, are designed to deal with cases where: (1) all we observe are …

Discontinuous Finite Element Methods (DFEM) have been widely used for solving $S_n$ radiation transport problems in participative and non-participative media. In the DFEM $S_n$ methodology, the transport equation is discretized into a set of algebraic equations that have to be solved for each spatial cell and angular d…

The paper studies heat kernels on weighted Riemannian manifolds with curvature bounds.

Paper optimizes WGAN parameters for non-Gaussian data.

Let M be a compact Riemannian manifold and E a Riemannian vector bundle on M. We look for hypersurfaces of E with a prescribed vertical Gaussian curvature. In trying to solve this problem fibre-wise, we loose the regularity of the resulting solution. To unsure the smoothness of the solution, we construct it as a radial…

Study on Kyle-Back model with risk aversion and non-Gaussian beliefs.

Study non-Gaussian measures' concentration properties in metric spaces.

Study on learning sparse fixed-structure Gaussian Bayesian networks with near-optimal sample complexity.

Unified framework solves nonlinear PDEs and IPs using Gaussian processes.

Novel method uses Gaussian process to estimate particle sizes from scattering data.