arXiv research

In this paper the unconditional stability of four well-known ADI schemes is analyzed in the application to time-dependent multidimensional diffusion equations with mixed derivative terms. Necessary and sufficient conditions on the parameter theta of each scheme are obtained that take into account the actual size of the…

We develop variation formulas for the quantities of extrinsic geometry for adapted variations of metrics on almost-product (e.g. foliated) Riemannian manifolds, and apply them to study the total mixed scalar curvature of a distribution -- analogue of the classical Einstein-Hilbert action. The mixed scalar curvature ${\…

Unified Minkowski problem discussed for (p,q)-mixed quermassintegrals.

Improves meta-learning efficiency with mixed-mode differentiation.

Study on gradient descent in Hilbert spaces with Markov chains, focusing on mixing coefficients.

This work improves mixing rates for Bayesian CART, a key component of BART.

We give a simple optimistic algorithm for which it is easy to derive regret bounds of $\tilde{O}(\sqrt{t_{\rm mix} SAT})$ after $T$ steps in uniformly ergodic Markov decision processes with $S$ states, $A$ actions, and mixing time parameter $t_{\rm mix}$. These bounds are the first regret bounds in the general, non-epi…

New framework relaxes independence assumption for graph-mixing dependencies.

We derive and analyze learning algorithms for apprenticeship learning, policy evaluation, and policy gradient for average reward criteria. Existing algorithms explicitly require an upper bound on the mixing time. In contrast, we build on ideas from Markov chain theory and derive sampling algorithms that do not require …

We consider the geodesic equation for the generalized Kahler potential with only mixed second derivatives bounded. We show that given such two generalized Kahler potentials, there is a unique geodesic segment such that for each point on the geodesic, the generalized Kahler potential has uniformly bounded mixed second d…

Study online learning in RKHS with dependent processes, focusing on \(β\)- and \(φ\)-mixing.

Mixed membership factorization is a popular approach for analyzing data sets that have within-sample heterogeneity. In recent years, several algorithms have been developed for mixed membership matrix factorization, but they only guarantee estimates from a local optimum. Here, we derive a global optimization (GOP) algor…

In this short paper, in order to price occupation-time options, such as (double-barrier) step options and quantile options, we derive various joint distributions of a mixed-exponential jump-diffusion process and its occupation times of intervals.

Mix-IRLS solves imbalanced mixed linear regression problems efficiently.

Study solves complex Hessian equation on Hermitian manifolds.

The mixed scalar curvature of a foliated Riemannian manifold, i.e., an averaged mixed sectional curvature, has been considered by several geometers. We explore the Yamabe type problem: to prescribe the constant mixed scalar curvature for a foliation by a conformal change of the metric in normal directions only. For a h…

Study shows DQN's performance degrades with temporal dependence in data.

Bayesian models that mix multiple Dirichlet prior parameters, called Multi-Dirichlet priors (MD) in this paper, are gaining popularity. Inferring mixing weights and parameters of mixed prior distributions seems tricky, as sums over Dirichlet parameters complicate the joint distribution of model parameters. This paper s…

The paper develops methods to derive mixed superposition rules for Lie systems and applies them to various physical systems.

New method for conformal prediction under Markovian data reduces coverage gap.

Paper analyzes distributed learning with non-i.i.d. samples.

The aim of this paper is to evaluate geometric Asian option by a mixed fractional subdiffusive Black-Scholes model. We derive a pricing formula for geometric Asian option when the underlying stock follows a time changed mixed fractional Brownian motion. We then apply the results to price Asian power options on the stoc…

The paper explores the relationship between joint mixability and negative dependence structures.

Bayesian optimisation tackles high-dimensional categorical and mixed search spaces.

Efficiently calibrates Bergomi models to VIX derivatives using vector quantization.

Develops a deep learning framework for various data types.

The paper proposes methods for predicting missing values in mixed data matrices.

Unified Bayesian Optimisation for mixed variables improves performance.

In this paper, we give a new sharp generalization bound of lp-MKL which is a generalized framework of multiple kernel learning (MKL) and imposes lp-mixed-norm regularization instead of l1-mixed-norm regularization. We utilize localization techniques to obtain the sharp learning rate. The bound is characterized by the d…

L21 SNF compresses mixed-sign data robustly.

This study deals with the problem of pricing compound options when the underlying asset follows a mixed fractional Brownian motion with jumps. An analytic formula for compound options is derived under the risk neutral measure. Then, these results are applied to value extendible options. Moreover, some special cases of …

Study geodesic equation on mixed-volume forms on balanced manifolds, proving existence of solutions.

Paper extends nonparametric regression bounds for dependent $\beta$-mixing samples.

New method recovers causal DAGs from general environments without strict assumptions.

SelectMix improves deep learning robustness against noisy labels.

New Krylov subspace methods speed up mixed-effects models with crossed random effects.

Functional determinant for mixed signature sphere products depends on sphere dimensions and parity.

Study pricing derivatives in markets with long-range dependence and jumps.

New dg-algebras generalize Brauer graph algebras, with applications to stability conditions and quadratic differentials.

New model integrates community and link clustering for network data.

Proves regularity of geodesic equation on Hermitian manifolds.

Paper analyzes Nyström regularization for time series forecasting with sequential sub-sampling.

New algorithm for sampling from distributions with thin tails.

The study explores mixed Killing vector fields on almost coKähler manifolds.

A universal framework for constructing confidence sets using sequential likelihood mixing.

This paper develops basic setting for the dual Orlicz-Brunn-Minkowski theory for star bodies. An Orlicz $\varphi$-radial addition of two or more star bodies is proposed and related dual Orlicz-Brunn-Minkowski inequality is established. Based on a linear Orlicz $\varphi$-radial addition of two star bodies, we derive a f…

Method completes mixed matrix from complex surveys with heterogeneous missingness.

Enhances gradient estimates for Hermitian Monge-Ampère equations.