arXiv research

Log-Normal Multiplicative Dynamics improves low-precision training of neural networks.

Two heuristics solve dynamic multiple travelling salesmen problems.

A powerful approach for understanding neural population dynamics is to extract low-dimensional trajectories from population recordings using dimensionality reduction methods. Current approaches for dimensionality reduction on neural data are limited to single population recordings, and can not identify dynamics embedde…

This work introduces a method to learn dynamical systems from noisy sensor measurements using multiple shooting.

Model for dynamic pricing across multiple RE groups to maximize revenue.

Paper proposes a method to estimate multiple dynamic quantiles jointly.

Enhances load forecasting for multiple entities with dynamic similarities.

Framework learns surrogates for molecular dynamics across multiple time-scales.

Proposes methods for learning optimal dynamic treatment regimes robust to unconfoundedness violations.

Study learns dynamics of linear systems from multiple short trajectories.

Bayesian methods improve tracking multiple objects through dynamic dependencies.

Proposes a new test for validating multivariate dynamic regression models.

Sequences of correlated binary patterns can represent many time-series data including text, movies, and biological signals. These patterns may be described by weighted combinations of a few dominant structures that underpin specific interactions among the binary elements. To extract the dominant correlation structures …

Generic model for commodity derivatives pricing.

Study optimizes resource allocation in noisy systems for better control.

New algorithms control FDX while achieving more power in online multiple testing.

We learn linear models from nonlinear systems using multiple trajectories and regularization.

We give a sharp lower bound for the number of geometrically distinct contractible periodic orbits of dynamically convex Reeb flows on prequantizations of symplectic manifolds that are not aspherical. Several consequences of this result are obtained, like a new proof that every bumpy Finsler metric on $S^n$ carries at l…

We present a set of models of the main stylized facts of market price fluctuations. These models comprise dynamical evolution with threshold dynamics and Langevin price equation with multiplicative noise, percolation models to describe the interaction between traders and hierarchical cascade models to unravel the possi…

MTAdam optimizes multiple loss terms in neural models, balancing gradients dynamically.

Paper introduces a novel method for dynamic covariance estimation with random forests.

The paper defines unimodularity for coisotropic Poisson spaces and discusses invariant volume forms.

New method optimizes multiple objectives using particle dynamics and gradient flow.

The Oseledets Multiplicative Ergodic theorem is a basic result with numerous applications throughout dynamical systems. These notes provide an introduction to this theorem, as well as subsequent generalizations. They are based on lectures at summer schools in Brazil, France, and Russia.

Dynamic residual adapters improve performance across multiple latent domains without domain labels.

The goal of system identification is to learn about underlying physics dynamics behind the time-series data. To model the probabilistic and nonparametric dynamics model, Gaussian process (GP) have been widely used; GP can estimate the uncertainty of prediction and avoid over-fitting. Traditional GPSSMs, however, are ba…

asp2vec learns dynamic node aspect distributions for better network embedding.

GAT-AGNN learns stock trends using graph and attention mechanisms.

Deep learning model predicts traffic flows across entire network for multiple steps ahead.

Agents learn and control complex mechanical systems through shared memories.

New method learns cell trajectories from multiple snapshots.

Algorithm learns multiple LDS models from unlabeled short trajectories.

We present a detailed analysis of interest rate derivatives valuation under credit risk and collateral modeling. We show how the credit and collateral extended valuation framework in Pallavicini et al (2011), and the related collateralized valuation measure, can be helpful in defining the key market rates underlying th…

WiGS improves active learning for regression by dynamically selecting informative samples.

The paper tackles fairness in forecasting and learning linear dynamical systems.

Solves portfolio optimization with costs using numerical methods.

Extracts causal brain dynamics across multiple scales.

Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other. Modeling and forecasting the behavior of such dynamics are generally not easy, due to the inherent hardness in understanding the complicated interactions and evolutions of their constituents. This paper introduce…

Geometric pruning rules improve change point detection in multiple time series.

This work extracts stochastic dynamical systems with $\alpha$-stable Lévy noise.

Systems of interacting particles or agents have wide applications in many disciplines such as Physics, Chemistry, Biology and Economics. These systems are governed by interaction laws, which are often unknown: estimating them from observation data is a fundamental task that can provide meaningful insights and accurate …

New algorithm learns switching dynamics from multiple neural signals.

D2PCCA integrates deep learning and probabilistic modeling for nonlinear dynamical systems.

We introduce a multiple curve framework that combines tractable dynamics and semi-analytic pricing formulas with positive interest rates and basis spreads. Negatives rates and positive spreads can also be accommodated in this framework. The dynamics of OIS and LIBOR rates are specified following the methodology of the …

Generative adversarial network for probabilistic forecasting of random systems.

Given a n-dimensional lamination endowed with a Riemannian metric, we introduce the notion of a multiplicative cocycle of rank d, where n and d are arbitrary positive integers. The holonomy cocycle of a foliation and its exterior powers as well as its tensor powers provide examples of multiplicative cocycles. Next, we …

Investigates market dynamics with informed traders and high-frequency traders.

We present a detailed study of the statistical properties of an Agent Based Model and of its generalization to the multiplicative dynamics. The aim of the model is to consider the minimal elements for the understanding of the origin of the Stylized Facts and their Self-Organization. The key elements are fundamentalist …