arXiv research

GOE statistics emerge from surface moduli space averages.

Study shows energy levels on hyperbolic surfaces follow GOE fluctuations.

Study smooth linear statistics on random covers of hyperbolic surfaces, showing central limit and variance results.

This is a sequel to the paper [Cas]. Here, we extend the methods of Farb-Wolfson using the theory of FI_G-modules to obtain stability of equivariant Galois representations of the etale cohomology of orbit configuration spaces. We establish subexponential bounds on the growth of unstable cohomology, and then use the Gro…

The Yukawa term in statistical mechanics quantifies information generation.

Inverse statistics in economics is considered. We argue that the natural candidate for such statistics is the investment horizons distribution. This distribution of waiting times needed to achieve a predefined level of return is obtained from (often detrended) historic asset prices. Such a distribution typically goes t…

We continue the study of a general class of spaces of 0-cycles on a manifold defined and begun by Farb-Wolfson-Wood. Using work of Gadish on linear subspace arrangements, we obtain representation stability for the cohomology of the ordered version of these spaces. We establish subexponential bounds on the growth of uns…

The paper shows Gaussian fluctuations in eigenvalue statistics of random hyperbolic surfaces.

Persistence diagrams from random matrices follow RMT universality, offering a new spectral diagnostic.

High-dimensional SGD limits show surprising dynamics and phase transitions.

Polyak step size GD reaches final radius of convergence after log iterations.

Study shows optimal spectral gaps diminish in large genus surfaces.

Model uses statistical physics principles to predict financial market volatility and returns.

A new method of moments estimator goes beyond data reweighting.

The paper improves ALO for $\ell_1$-regularized models.

The inverse statistics is the distribution of waiting times needed to achieve a predefined level of return obtained from (detrended) historic asset prices \cite{optihori,gainloss}. Such a distribution typically goes through a maximum at a time coined the {\em optimal investment horizon}, $τ^*_ρ$, which defines the most…

Loose bounds found for least-norm interpolant in over-parameterized settings.

When can reliable inference be drawn in the "Big Data" context? This paper presents a framework for answering this fundamental question in the context of correlation mining, with implications for general large scale inference. In large scale data applications like genomics, connectomics, and eco-informatics the dataset…

In this paper, we present a multi-period trading model in the style of Kyle (1985)'s inside trading model, by assuming that there are at least two insiders in the market with long-lived private information, under the requirement that each insider publicly discloses his stock trades after the fact. Based on this model, …

The Mallows model, introduced in the seminal paper of Mallows 1957, is one of the most fundamental ranking distribution over the symmetric group $S_m$. To analyze more complex ranking data, several studies considered the Generalized Mallows model defined by Fligner and Verducci 1986. Despite the significant research in…

Study on kernel methods in large-scale machine learning problems.

Study eigenvector overlaps in large Gaussian matrices, simplifying for GOE.

Improved computational complexity in statistical models using second-order information.

In recent publications, the authors have considered inverse statistics of the Dow Jones Industrial Averaged (DJIA) [1-3]. Specifically, we argued that the natural candidate for such statistics is the investment horizons distribution. This is the distribution of waiting times needed to achieve a predefined level of retu…

We show that, given an immortal solution to the Ricci flow on a closed manifold with uniformly bounded curvature and diameter, the Ricci tensor goes to zero as t goes to infinity. We also show that if there exists an immortal solution on a closed 3-dimensional manifold such that the product of the square of the diamete…

Develops unbiased estimation method using underdamped Langevin dynamics.

Proposes an exponentially increasing step-size for faster parameter estimation in statistical models.

Study shows exponential growth of Laplacian determinant on random hyperbolic surfaces.

This paper investigates asymptotic behaviors of gradient descent algorithms (particularly accelerated gradient descent and stochastic gradient descent) in the context of stochastic optimization arising in statistics and machine learning where objective functions are estimated from available data. We show that these alg…

The parametric complexity is the key quantity in the minimum description length (MDL) approach to statistical model selection. Rissanen and others have shown that the parametric complexity of a statistical model approaches a simple function of the Fisher information volume of the model as the sample size $n$ goes to in…

We introduce and study a new family of extensions for the Borsuk-Ulam and topological Radon type theorems. The defining idea for this new family is to replace requirements of the form `a subset that is large in some sense goes to a singleton' with requirements of the milder form `a subset that is large in some sense go…

We consider the problem of statistical inference for ranking data, specifically rank aggregation, under the assumption that samples are incomplete in the sense of not comprising all choice alternatives. In contrast to most existing methods, we explicitly model the process of turning a full ranking into an incomplete on…

Stochastic gradient descent converges to universal limits in high dimensions.

It is the aim of this article to determine curvature quantities of an arbitrary Riemannian monotone metric on the space of positive matrices resp. nonsingular density matrices. Special interest is focused on the scalar curvature due to its expected quantum statistical meaning. The scalar curvature is explained in more …

Unified approach for quantum and classical learning from evaluation oracles.

Study the limits of discrete DPPs to continuous DPPs as set size grows.

Paper studies MCCR models with scale parameters tending to zero, revealing optimal learning rate and comparing robustness.

Let M be a closed connected manifold. Let m(M) be the Morse number of M, that is, the minimal number of critical points of a Morse function on M. Let N be a finite cover of M of degree d. M.Gromov posed the following question: what are the asymptotic properties of m(N) as d goes to infinity? In this paper we study the …

Study calculates eigenvalues and eigenfunctions for spherical triangles and finds fundamental gap behavior.

Inferring the correct answers to binary tasks based on multiple noisy answers in an unsupervised manner has emerged as the canonical question for micro-task crowdsourcing or more generally aggregating opinions. In graphon estimation, one is interested in estimating edge intensities or probabilities between nodes using …

Paper tackles small eigen-gap estimation and inference for noisy symmetric matrices.

Study improves Hayashi-Yoshida estimator for high-dimensional stock covolatility.

A statistical predictive model in which a high-dimensional time-series regenerates at the end of each day is used to model road traffic. Due to the regeneration, prediction is based on a daily modeling using a vector autoregressive model that combines linearly the past observations of the day. Due to the high-dimension…

We show that univariate and symmetric multivariate Hawkes processes are only weakly causal: the true log-likelihoods of real and reversed event time vectors are almost equal, thus parameter estimation via maximum likelihood only weakly depends on the direction of the arrow of time. In ideal (synthetic) conditions, test…

New method detects OOD samples using neural network trajectories.

Social, technological and economic time series are divided by events which are usually assumed to be random albeit with some hierarchical structure. It is well known that the interevent statistics observed in these contexts differs from the Poissonian profile by being long-tailed distributed with resting and active per…

New DP bootstrap method for statistical inference with improved privacy and accuracy.

We construct a new family {K_n} of simply connected symplectic 4-manifolds with the property c_1^2(K_n)/chi(K_n) -> 9 (as n goes to infinity).