A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New algorithm adapts to unknown demand smoothness for dynamic pricing.
New metric improves latent dynamics inference from neural data.
Efficient EP algorithm improves smoothing distribution inference in financial models.
The paper is an informal report on joint work with Stefan Haller on Dynamics in relation with Topology and Spectral Geometry. By dynamics one means a smooth vector field on a closed smooth manifold; the elements of dynamics of concern are the rest points, instantons and closed trajectories. One discusses their counting…
New algorithms reduce dynamic regret for convex and smooth functions in non-stationary environments.
We prove that a topological contact isotopy uniquely defines a topological contact Hamiltonian. Combined with previous results from [MS11], this generalizes the classical one-to-one correspondence between smooth contact isotopies and their generating smooth contact Hamiltonians and conformal factors to the group of top…
Smooth calibration improves forecast reliability even with leaked information.
Improved dynamic regret analysis for strongly convex and smooth functions.
Study bounds topological entropy of toroidal attractors.
FLUID uses flows to unify filtering and smoothing for complex systems.
Whereas subriemannian geometry usually deals with smooth horizontal distributions, partially hyperbolic dynamical systems provide many examples of subriemannian geometries defined by non-smooth (namely, Hölder continuous) distributions. These distributions are of great significance for the behavior of the parent dynami…
Prediction of dynamical time series with additive noise using support vector machines or kernel based regression has been proved to be consistent for certain classes of discrete dynamical systems. Consistency implies that these methods are effective at computing the expected value of a point at a future time given the …
Study nonconcave portfolio choice with smooth ambiguity and Bayesian learning.
Diffusion models improve creativity by smoothing the score function, leading to interpolated data.
The paper develops techniques to study dynamical systems with Carnot metrics.
New method approximates sampling from smooth potential distributions using a vanishing penalty.
Develops theory for data-driven methods in dynamical systems.
A dynamical analog of the prime ideals for simple non-commutative rings is introduced. We prove a factorization theorem for the dynamical ideals. The result is used to classify the surface knots and links in the smooth 4-dimensional manifolds.
Floer constructs homology from flow lines in generalized dynamical systems and combinatorial vector fields.
Smooth parametrization consists in a subdivision of the mathematical objects under consideration into simple pieces, and then parametric representation of each piece, while keeping control of high order derivatives. The main goal of the present paper is to provide a short overview of some results and open problems on s…
The paper improves competitive and dynamic regret bounds for smoothed online learning.
Inverse depth scaling found in LLMs due to similar layers averaging error.
Proves rigidity of 3D partially hyperbolic systems via autonomous dynamics.
A new dynamic learning-rate scheme for optimization.
New model predicts energy prices volatility by smoothing time variation and persistence.
In unsupervised learning, there is no apparent straightforward cost function that can capture the significant factors of variations and similarities. Since natural systems have smooth dynamics, an opportunity is lost if an unsupervised objective function remains static during the training process. The absence of concre…
In this paper we show that several dynamical systems with time delay can be described as vector fields associated to smooth functions via a bracket of Leibniz structure. Some examples illustrate the theoretical considerations.
Transformers handle infinite dimensional inputs effectively by feature extraction and dynamic feature selection.
Paper tackles dynamic behavior of variable topology mechanisms, presenting new transition conditions.
In this paper, we analyse classical variants of the Spectral Clustering (SC) algorithm in the Dynamic Stochastic Block Model (DSBM). Existing results show that, in the relatively sparse case where the expected degree grows logarithmically with the number of nodes, guarantees in the static case can be extended to the dy…
Dynamic angles estimated from noisy measurements over time with smoothness constraints.
A new method for generating samples without training, using smoothed score matching.
SPH-ParVI uses fluid dynamics to sample unknown densities efficiently.
Paper defines dynamical coherence for flows and proves it under specific conditions.
Study stabilizes second-order systems to first-order dynamics.
State-space smoothing has found many applications in science and engineering. Under linear and Gaussian assumptions, smoothed estimates can be obtained using efficient recursions, for example Rauch-Tung-Striebel and Mayne-Fraser algorithms. Such schemes are equivalent to linear algebraic techniques that minimize a conv…
Time series analysis is used to understand and predict dynamic processes, including evolving demands in business, weather, markets, and biological rhythms. Exponential smoothing is used in all these domains to obtain simple interpretable models of time series and to forecast future values. Despite its popularity, expon…
Dynamic programming (DP) solves a variety of structured combinatorial problems by iteratively breaking them down into smaller subproblems. In spite of their versatility, DP algorithms are usually non-differentiable, which hampers their use as a layer in neural networks trained by backpropagation. To address this issue,…
Estimates multiple linear systems on a graph with smoothness constraints.
Smooth flows for physical systems with smooth energies and forces.
We establish a connection between trend filtering and system identification which results in a family of new identification methods for linear, time-varying (LTV) dynamical models based on convex optimization. We demonstrate how the design of the cost function promotes a model with either a continuous change in dynamic…
Latent variable models have been widely applied for the analysis of time series resulting from experimental neuroscience techniques. In these datasets, observations are relatively smooth and possibly nonlinear. We present Variational Inference for Nonlinear Dynamics (VIND), a variational inference framework that is abl…
In this paper, we propose a reinforcement learning-based algorithm for trajectory optimization for constrained dynamical systems. This problem is motivated by the fact that for most robotic systems, the dynamics may not always be known. Generating smooth, dynamically feasible trajectories could be difficult for such sy…
New stability estimate for metric rigidity in hyperbolic dynamics.
State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference \emph{and learning} (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gauss…
We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by non-parametric Gaussian process (GP) models. GPs are gaining increasing importance in signal processing, machine learning,…
The Ricci flow is a parabolic evolution equation in the space of Riemannian metrics of a smooth manifold. To some extent, Einstein equations give rise to a similar hyperbolic evolution. The present text is an introductory exposition to Bianchi-Ricci and Bianchi-Einstein flows, that is, the restricted finitely dimension…
Framework for continuous-time network data representation learning.