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…

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 …

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.

Study on 2-valued dynamics on complex plane, showing some dynamics can't be group actions.

The paper studies dynamic star-shaped risk measures and their representation.

Study circles to understand dynamics and rigidity in homogeneous spaces.

A Riemmanian foliated dynamical system of 3-dimension $(\mathrm{RFDS}^{3})$ is a closed Riemannian 3-manifold with additional structures: foliation, dynamical system. In the context of arithmetic topology, it is a geometric/analytic analogue of an arithmetic scheme with a conjectural dynamical system suggested by C. De…

Paper connects dynamics of mechanical systems to Reeb dynamics.

Two heuristics solve dynamic multiple travelling salesmen problems.

In this paper we present a theoretical framework for studying coherent acceptability indices in a dynamic setup. We study dynamic coherent acceptability indices and dynamic coherent risk measures, and we establish a duality between them. We derive a representation theorem for dynamic coherent risk measures in terms of …

DOODL learns shared spectral dynamics across related dynamical systems.

We propose a new class of mappings, called Dynamic Limit Growth Indices, that are designed to measure the long-run performance of a financial portfolio in discrete time setup. We study various important properties for this new class of measures, and in particular, we provide necessary and sufficient condition for a Dyn…

In this paper we present a theoretical framework for determining dynamic ask and bid prices of derivatives using the theory of dynamic coherent acceptability indices in discrete time. We prove a version of the First Fundamental Theorem of Asset Pricing using the dynamic coherent risk measures. We introduce the dynamic …

Dynamical-VAE learns causal dynamics from POMDPs using future information.

Most real world phenomena such as sunlight distribution under a forest canopy, minerals concentration, stock valuation, exhibit nonstationary dynamics i.e. phenomenon variation changes depending on the locality. Nonstationary dynamics pose both theoretical and practical challenges to statistical machine learning algori…

Unified analysis of DLNs using DMFT reveals dynamics of loss convergence and generalization trade-offs.

dLDS models neural dynamics as sparse combinations of simpler components.

Reinforcement learning would enjoy better success on real-world problems if domain knowledge could be imparted to the algorithm by the modelers. Most problems have both hidden state and unknown dynamics. Partially observable Markov decision processes (POMDPs) allow for the modeling of both. Unfortunately, they do not p…

We consider trivializations of second iterated bundles of a Lie group that preserve lifted group structures. With such a trivialization, we elaborate Hamiltonian dynamics on cotangent, Lagrangian dynamics on tangent bundles and, both Hamiltonian and Lagrangian dynamics on Tulczyjew's symplectic space which is tangent o…

Framework for quantifying uncertainty in dynamic processes.

This survey clarifies dynamic network terminology and reviews GNN models for dynamic networks.

Framework infers Langevin dynamics from stochastic observations of latent systems.

The paper extends Vlasov kinetic theory to time-dependent dynamics using cosymplectic and cocontact manifolds.

NDS learns dynamical models with prior knowledge, improving accuracy and efficiency.

New method learns population dynamics from snapshots, outperforming existing models.

Paper uses Chebyshev Tensors for accurate dynamic sensitivities and ISDA SIMM computation.

Model dynamic customer sensitivities across categories.

Survey on computational models in dynamical systems, including new universality concepts.

FNSDA adapts to new dynamics via Fourier space adaptation.

Study on geometry and dynamics of transverse subgroups.

Dynamic risk assessment method for WUI fires improves upon static frameworks.