arXiv research

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

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…

SHA improves fMRI alignment for cognitive state discovery.

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 …

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

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.