arXiv research

The paper analyzes convergence of Langevin dynamics with time-dependent metrics.

Study of SGD with state-dependent noise, improving escape from local minima.

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

Stable topological summary captures evolving dependency structure in dynamic Bayesian networks.

Novel model captures high-dimensional copulas with spectral dynamics and regularization.

Paper introduces a new volatility model for natural gas markets and discusses swing option pricing.

Proposes a Koopman operator method for time-dependent reliability analysis of nonlinear systems.

New framework handles dynamic contexts in reinforcement learning.

Study forecasts stock returns on JSE using SGDLMs capturing cross-series dependencies.

This paper explores the dependence modeling of financial assets in a dynamic way and its critical role in measuring risk. Two new methods, called Accelerated Moving Window method and Bottom-up method are proposed to detect the change of copula. The performance of these two methods together with Binary Segmentation \cit…

We investigate the dynamics of growth models in terms of dynamical system theory. We analyse some forms of knowledge and its influence on economic growth. We assume that the rate of change of knowledge depends on both the rate of change of physical and human capital. First, we study model with constant savings. The mod…

Study on identifying and inferring nonlinear dynamics on unknown networks.

Paper models non-linear dynamics from time series data.

Proposes a new test for validating multivariate dynamic regression models.

We develop dependent hierarchical normalized random measures and apply them to dynamic topic modeling. The dependency arises via superposition, subsampling and point transition on the underlying Poisson processes of these measures. The measures used include normalised generalised Gamma processes that demonstrate power …

Dynamic classifier chains with XGBoost reduces multi-label classification costs and improves label dependency handling.

Dynamic Vine Copulas detect and quantify time-varying higher-order interactions in multivariate systems.

Study shows how mini-batch GD with random reshuffling affects least squares regression dynamics.

We consider a stochastic model for the dynamics of the two-sided limit order book (LOB). Our model is flexible enough to allow for a dependence of the price dynamics on volumes. For the joint dynamics of best bid and ask prices and the standing buy and sell volume densities, we derive a functional limit theorem, which …

We consider the dynamics of a smart grid system characterized by widespread distributed generation and storage devices. We assume that agents are free to trade electric energy over the network and we focus on the emerging market dynamics. We consider three different models for the market dynamics for which we present a…

New bounds for SGD generalize without mutual information terms.

A new model prices assets considering market microstructure effects.

Bayesian methods improve tracking multiple objects through dynamic dependencies.

Understanding temporal dynamics has proved to be highly valuable for accurate recommendation. Sequential recommenders have been successful in modeling the dynamics of users and items over time. However, while different model architectures excel at capturing various temporal ranges or dynamics, distinct application cont…

Oddnet detects anomalies in dynamic networks using time series methods.

PAC-Bayesian theory applied to data-dependent hypothesis sets yields uniform generalization bounds.

Efficiently selects top-m designs for various contexts using sequential sampling.

Model predicts bid and ask price dynamics with spread-dependent intensities.

Investigates optimal portfolio strategies in markets with latent side information.

Paper introduces MSPD for multivariate risk processes with dependencies.

Proposes a neural network for dynamic risk prediction of AMD using longitudinal fundus images.

CFTM uses fractional Brownian motion for dynamic topic modeling.

New model analyzes dynamic correlations in stock returns.

LOV model calibrates European and American options with path-dependent volatility.

The paper offers error bounds for quantized dynamical models.

There is intense interest in understanding the stochastic and dynamical properties of the global Foreign Exchange (FX) market, whose daily transactions exceed one trillion US dollars. This is a formidable task since the FX market is characterized by a web of fluctuating exchange rates, with subtle inter-dependencies wh…

Deep learning models price convertible bonds with complex reset and call features.

New algorithm learns switching dynamics from multiple neural signals.

Framework learns inter-electronic potential for molecular dynamics.

SALT models combine ARHMM and SLDS for efficient, interpretable time-series analysis.

Training-free model learns SDE dynamics without training, accelerating parameter studies.

We consider reinforcement learning in input-driven environments, where an exogenous, stochastic input process affects the dynamics of the system. Input processes arise in many applications, including queuing systems, robotics control with disturbances, and object tracking. Since the state dynamics and rewards depend on…

DiPCA algorithm improves scalability and solution quality for time-dependent data.

We present a dynamical many-body theory of money in which the value of money is a time dependent ``strategic variable'' that is chosen by the individual agents. The value of money in equilibrium is not fixed by the equations, and thus represents a continuous symmetry. The dynamics breaks this continuous symmetry by fix…

Chronological Causal Bandits (CCB) tackles dynamic causal decision-making.

Study analyzes stock market dynamics using Tsallis statistics and GHE, revealing pre-bubble and post-bubble market characteristics.

Study optimal control in unknown nonlinear systems with near-optimal regret bound.

Extends unbiased simulation method to Asian options.