arXiv research

New method solves quantile crossing problem in econometrics.

Proposes a new method to improve Bayesian computation accuracy using flexible classification.

Scalable model checking for stochastic systems using Gaussian Processes and Bayesian Neural Networks.

We give a definition of an integer-valued function $\sum_i α_i x ^*_i$ derived from arrow diagrams for the ambient isotopy classes of oriented spherical curves. Then, we introduce certain elements of the free $\mathbb{Z}$-module generated by the arrow diagrams with at most $l$ arrows, called relators of Type~($\check{…

Improves neural network estimates using IFs without needing more data.

IQ-BART models conditional quantiles using a non-parametric Bayesian approach.

Time-aware fact-checking improves veracity predictions for time-sensitive claims.

Proposes flexible spatial models for better understanding spatial heterogeneity.

Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party …

Paper formalizes Simon's satisficing through FFSD, proving its equivalence to expected utility theory.

By the SYZ construction, a mirror pair $(X,\check{X})$ of a complex torus $X$ and a mirror partner $\check{X}$ of the complex torus $X$ is described as the special Lagrangian torus fibrations $X \rightarrow B$ and $\check{X} \rightarrow B$ on the same base space $B$. Then, by the SYZ transform, we can construct a simpl…

Study combines SEM, OLS, and DML for robustness checks in survey-based research.

Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This paper provides a gentle introduction to non-parametric Bayesian modeling of complex networks: Using an infinite mixture model …

We propose a semiparametric test to evaluate (i) whether different instruments induce subpopulations of compliers with the same observable characteristics on average, and (ii) whether compliers have observable characteristics that are the same as the full population on average. The test is a flexible robustness check f…

The chapter improves deep learning models by interpreting and improving their performance.

This paper deforms complex tori and their mirrors using gerbes.

Study how past radiation determines present matter in Penrose's cyclic cosmology.

Statistical model checking for PCTL on MDPs using reinforcement learning.

Research aims to make fact-checking models more transparent.

A machine-checked Itô calculus for Brownian motion on $[0,T]$

Safe imitation learning with a safety layer for flexible training.

In this paper, we define, from a finite set E of functions, a family of holomorphic webs ${\cal W}(n;E)$ of codimension one in any dimension $n$. We prove that it is sufficient to check a finite number of conditions for these webs to be all ordinary and to have all maximal rank. Moreover, these conditions may be prac…

Assume that $M$ is a smooth manifold with a symplectic structure $ω$. Then Weyl manifolds on the symplectic manifold $M$ are Weyl algebra bundles endowed with suitable transition functions. From the geometrical point of view, Weyl manifolds can be regarded as geometrizations of star products attached to $(M,ω)$. In the…

We study the $(2+2)$-Einstein warped product manifolds, where the scalar curvature of the Base is a multiple of the warping function, and we called this condition (inside a warped product manifold) $f$-curvature-Base ($R_{f_B}$).The aim of this paper is to check if there are Base-manifolds with non-flat metrics that sa…

In a recent paper in this journal [J. Stat. Mech. (2009) P02037] we proposed a new, physically motivated, distribution function for modeling individual incomes having its roots in the framework of the k-generalized statistical mechanics. The performance of the k-generalized distribution was checked against real data on…

DC-Check helps guide ML development by considering data-centric aspects.

Adma proposes a flexible loss function for neural networks.

The paper improves SBI for BHMs by diagnosing misspecification and inferring parameters.

Deep learning aids ADMM-based decoding for binary linear codes.

Extends matrix factorization for deviance-based losses with GLM theory.

FinReflectKG builds a comprehensive financial knowledge graph from SEC filings, improving extraction quality.

Developed a machine-checked Itô calculus for Brownian motion.

Proves SYZ mirror symmetry for del Pezzo and rational elliptic surfaces.

This paper describes a new method of bond portfolio optimization based on stochastic string models of correlation structure in bond returns. The paper shows how to approximate correlation function of bond returns, compute the optimal portfolio allocation using Wiener-Hopf factorization, and check whether a collection o…

Social networks are getting closer to our real physical world. People share the exact location and time of their check-ins and are influenced by their friends. Modeling the spatio-temporal behavior of users in social networks is of great importance for predicting the future behavior of users, controlling the users' mov…

We describe the infinitesimal moduli space of pairs $(Y, V)$ where $Y$ is a manifold with $G_2$ holonomy, and $V$ is a vector bundle on $Y$ with an instanton connection. These structures arise in connection to the moduli space of heterotic string compactifications on compact and non-compact seven dimensional spaces, e.…

We prove that a real-valued function (that is not assumed to be continuous) on a real analytic manifold is analytic whenever all its restrictions to analytic submanifolds homeomorphic to the 2-sphere are analytic. This is a real analog for the classical theorem of Hartogs that a function on a complex manifold is comple…

We prove the following result announced in Todorov and Valov: Any homogeneous, metric $ANR$-continuum is a $V^n_G$-continuum provided $\dim_GX=n\geq 1$ and $\check{H}^n(X;G)\neq 0$, where $G$ is a principal ideal domain. This implies that any homogeneous $n$-dimensional metric $ANR$-continuum with $\check{H}^n(X;G)\neq…

$Q$-learning with function approximation is one of the most popular methods in reinforcement learning. Though the idea of using function approximation was proposed at least 60 years ago, even in the simplest setup, i.e, approximating $Q$-functions with linear functions, it is still an open problem on how to design a pr…

ESOP uses Bayesian optimization to find optimal lock-down schedules.

We specify a result of Yokoi \cite{yo} by proving that if $G$ is an abelian group and $X$ is a homogeneous metric $ANR$ compactum with $\dim_GX=n$ and $\check{H}^n(X;G)\neq 0$, then $X$ is an $(n,G)$-bubble. This implies that any such space $X$ has the following properties: $\check{H}^{n-1}(A;G)\neq 0$ for every closed…

Paper checks SSC for matrix factorizations using Gurobi.

Study robust learning of Lipschitz functions under corrupted binary signals.

Hi-fi priors enhance BNNs by learning flexible activations.

Study Mabuchi rays on toric Kähler manifolds to understand quantization.

New method evaluates language model forecasters by checking consistency of predictions.

Next point-of-interest (POI) recommendation aims to offer suggestions on which POI to visit next, given a user's POI visit history. This problem has a wide application in the tourism industry, and it is gaining an increasing interest as more POI check-in data become available. The problem is often modeled as a sequenti…

Deep learning improves quantile regression for censored survival data.