arXiv research

The paper introduces a dynamic MVP model using high-frequency financial data.

We solve the paradox of score-based methods by minimizing path variance.

Multivariate Pattern (MVP) classification can map different cognitive states to the brain tasks. One of the main challenges in MVP analysis is validating the generated results across subjects. However, analyzing multi-subject fMRI data requires accurate functional alignments between neuronal activities of different sub…

In this paper, we consider equilibrium strategies under Volterra processes and time-inconsistent preferences embracing mean-variance portfolio selection (MVP). Using a functional Itô calculus approach, we overcome the non-Markovian and non-semimartingale difficulty in Volterra processes. The equilibrium strategy is the…

The multivariate probit model (MVP) is a popular classic model for studying binary responses of multiple entities. Nevertheless, the computational challenge of learning the MVP model, given that its likelihood involves integrating over a multidimensional constrained space of latent variables, significantly limits its a…

In order to decode the human brain, Multivariate Pattern (MVP) classification generates cognitive models by using functional Magnetic Resonance Imaging (fMRI) datasets. As a standard pipeline in the MVP analysis, brain patterns in multi-subject fMRI dataset must be mapped to a shared space and then a classification mod…

Hyperalignment has been widely employed in Multivariate Pattern (MVP) analysis to discover the cognitive states in the human brains based on multi-subject functional Magnetic Resonance Imaging (fMRI) datasets. Most of the existing HA methods utilized unsupervised approaches, where they only maximized the correlation be…

In this short report, we discuss how coordinate-wise descent algorithms can be used to solve minimum variance portfolio (MVP) problems in which the portfolio weights are constrained by $l_{q}$ norms, where $1\leq q \leq 2$. A portfolio which weights are regularised by such norms is called a sparse portfolio (Brodie et …

Study compares three portfolio optimization methods on Indian stocks.

This study compares three portfolio design approaches for stock selection.

Multivariate Pattern (MVP) classification holds enormous potential for decoding visual stimuli in the human brain by employing task-based fMRI data sets. There is a wide range of challenges in the MVP techniques, i.e. decreasing noise and sparsity, defining effective regions of interest (ROIs), visualizing results, and…

Improved analysis of UCBVI algorithm with better empirical performance.

New algorithm reduces reinforcement learning complexity, approaching contextual bandits.

The Lagrangian representation of multi-Hamiltonian PDEs has been introduced by Y. Nutku and one of us (MVP). In this paper we focus on systems which are (at least) bi-Hamiltonian by a pair $A_1$, $A_2$, where $A_1$ is a hydrodynamic-type Hamiltonian operator. We prove that finding the Lagrangian representation is equiv…

This study compares three portfolio optimization methods on Indian stocks.

New algorithms reduce regret in both stochastic and deterministic environments.

Improved reinforcement learning for episodes with varying action sets.

End-to-end framework optimizes financial metrics using neural networks.

Study benchmarks cryptocurrency risk using GBM, revealing Lognormal limitations.

New algorithm reduces MDP regret by accounting for state suboptimality gaps and variance.

The paper establishes maximum principles and stochastic completeness for pseudo-Hermitian manifolds.

Islamic banks being commercial entities strive to earn profit within shariah ambit. Therefore, they seem to be basing themselves upon two knowledge streams namely i) Islamic jurisprudence principles, and ii) banking principles. Islamic jurisprudence principles primarily aim at bringing shariah compliance while banking …

The h-principle helps solve complex geometric problems.

The study establishes uncertainty principles on harmonic manifolds of rank one.

Shows flexible sheaves as fibrant objects for Gromov's h-principle.

A new method to break down insurance costs into risk and uncertainty.

Study on maximum principles for nonlinear equations on Riemannian manifolds.

Derives Fredholm criteria for isotypical components from a Simonenko principle.

Establishes a boundary maximum principle for varifolds with fixed contact angle.

Study proves Maximum Principles for unbounded Riemannian domains.

A pricing principle is introduced for non-attainable claims in incomplete markets.

We show that a well known uncertainty principle for functions on the circle can be derived from an uncertainty principle for the Euclidean motion group.

Proves a principle for one-phase Bernoulli problem minimizers.

New method proves $h$-principles for stable forms on manifolds.

We give a version of the comparison principle from pluripotential theory where the Monge-Ampère measure is replaced by the Bergman kernel and use it to derive a maximum principle

In this paper, we develop a new mathematical technique which allows us to express the joint distribution of a Markov process and its running maximum (or minimum) through the marginal distribution of the process itself. This technique is an extension of the classical reflection principle for Brownian motion, and it is o…

Note establishes a local maximum principle for Ricci flow under curvature conditions.

Along with fruitful applications of Deep Neural Networks (DNNs) to realistic problems, recently, some empirical studies of DNNs reported a universal phenomenon of Frequency Principle (F-Principle): a DNN tends to learn a target function from low to high frequencies during the training. The F-Principle has been very use…

Derives time-averaged active inference from control principles.

New principle for harmonic maps helps study higher-dimensional submanifolds.

In this paper we give three applications of a method to prove h-principles on closed manifolds. Under weaker conditions this method proves a homological h-principle, under stronger conditions it proves a homotopical one. The three applications are as follows: a homotopical version of Vassiliev's h-principle, the contra…

In this paper we prove two extensions of Hamilton's maximal principle for systems pf parabolic equations which sould be useful for the study of the Ricci flow and some other geometric evolution equations. One extension is a time-dependent maximum principle and the other is a time-dependent maximum principle subject to …

We present a new statistical learning paradigm for Boltzmann machines based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from Jaynes maximum entropy principle and from standard maximum likelihood estimation.We demonstrate the LME principle BY deriving …

3-manifolds study Hasse norm principle, akin to number fields.

New equivalences found linking parabolicity, comparison principle, and capacity on Riemannian manifolds.

Maps to manifolds transverse to certain distributions satisfy an $h$-principle.

Paper proves h-principles for symplectic structures and foliations.

Previous studies have shown that deep neural networks (DNNs) with common settings often capture target functions from low to high frequency, which is called Frequency Principle (F-Principle). It has also been shown that F-Principle can provide an understanding to the often observed good generalization ability of DNNs. …