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 proof of generalized Chow-Rashevskii theorem for non-linear systems.
New methods prove controllability of non-linear systems, extending classical results.
New method disentangles perceptual uncertainty and behavioral costs in partially observable systems.
Neural ODEs control graph dynamics with low energy feedback.
The paper analyzes deep neural networks using control theory to set a time limit for their convergence.
We present a deep recurrent neural network architecture to solve a class of stochastic optimal control problems described by fully nonlinear Hamilton Jacobi Bellmanpartial differential equations. Such PDEs arise when one considers stochastic dynamics characterized by uncertainties that are additive and control multipli…
Safe RL in linear systems achieves -regret.
This research evaluates learning models for bionic robots, focusing on transfer function identification.
Paper models non-linear dynamics from time series data.
Modeling dynamical systems is important in many disciplines, e.g., control, robotics, or neurotechnology. Commonly the state of these systems is not directly observed, but only available through noisy and potentially high-dimensional observations. In these cases, system identification, i.e., finding the measurement map…
Unified theory of -expectations derived from chaotic dynamics.
Two proofs of Kalman Theorem using flows of vector fields.
Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based on either the conceived "worst" case scenario or a few typical operation scenarios. These schemes are facing significant adaptiveness and r…
We introduce Embed to Control (E2C), a method for model learning and control of non-linear dynamical systems from raw pixel images. E2C consists of a deep generative model, belonging to the family of variational autoencoders, that learns to generate image trajectories from a latent space in which the dynamics is constr…
New framework for online control in evolving populations.
We introduce a prototype model in an attempt to capture some aspects of market dynamics simulating a trading mechanism. The model description starts with a discrete-space, continuous-time Markov process describing arrival and movement of orders with different prices. We then perform a re-scaling procedure leading to a …
We propose a framework for modeling and estimating the state of controlled dynamical systems, where an agent can affect the system through actions and receives partial observations. Based on this framework, we propose the Predictive State Representation with Random Fourier Features (RFFPSR). A key property in RFF-PSRs …
Hybrid controller combines model-based and policy-based reinforcement learning.
This paper studies how gradient descent in control systems can perform well on unseen data.
New framework for analyzing games with multi-dimensional singular controls and non-linear jumps.
R package for online forecasting in various fields.
Broadens Jourdain and Martini's method to non-linear stochastic processes.
New insights into RL efficiency from managing time discretization.
This work introduces the concept of tangent space regularization for neural-network models of dynamical systems. The tangent space to the dynamics function of many physical systems of interest in control applications exhibits useful properties, e.g., smoothness, motivating regularization of the model Jacobian along sys…
SSL framework identifies non-linear systems without labeled data.
Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown grea…
Learning weights in a spiking neural network with hidden neurons, using local, stable and online rules, to control non-linear body dynamics is an open problem. Here, we employ a supervised scheme, Feedback-based Online Local Learning Of Weights (FOLLOW), to train a network of heterogeneous spiking neurons with hidden l…
We study the problem to provide a triangular form based on implicit differential equations for non-linear multi-input systems with respect to the flatness property. Furthermore, we suggest a constructive method for the transformation of a given system into that special triangular shape, if possible. The well known Brun…
The paper proves stability of certain cosmological models with negative spatial curvature.
Complex non-linear interactions between banks and assets we model by two time-dependent Erdős Renyi network models where each node, representing bank, can invest either to a single asset (model I) or multiple assets (model II). We use dynamical network approach to evaluate the collective financial failure---systemic ri…
Model learns and plans in real-time under constraints for robotic systems.
Paper tackles robust control of SDEs with ambiguity, proving value function existence and applying to investment problems.
Develops inverse EKF for non-linear systems with stability guarantees and learning unknown dynamics.
Introduces TDRC to balance TD's ease and soundness.
Latent force models are systems whereby there is a mechanistic model describing the dynamics of the system state, with some unknown forcing term that is approximated with a Gaussian process. If such dynamics are non-linear, it can be difficult to estimate the posterior state and forcing term jointly, particularly when …
We develop algorithms to learn non-linear dynamical systems without mixing assumptions.
In this work we introduce two novel deterministic annealing based clustering algorithms to address the problem of Edge Controller Placement (ECP) in wireless edge networks. These networks lie at the core of the fifth generation (5G) wireless systems and beyond. These algorithms, ECP-LL and ECP-LB, address the dominant …
Proves energy estimates for tensorial wave equations, decoupling components for stability proof.
On-line estimation plays an important role in process control and monitoring. Obtaining a theoretical solution to the simultaneous state-parameter estimation problem for non-linear stochastic systems involves solving complex multi-dimensional integrals that are not amenable to analytical solution. While basic sequentia…
Paper uses non-linear dimension reduction for better economic forecasting.
Learning optimal feedback control laws capable of executing optimal trajectories is essential for many robotic applications. Such policies can be learned using reinforcement learning or planned using optimal control. While reinforcement learning is sample inefficient, optimal control only plans an optimal trajectory fr…
Agent optimizes perpetual contract liquidation with transaction costs and risk.
We solve a complex Bayesian control problem with novel methods.
This paper uses NLDT to find interpretable control rules from complex DRL policies.
This work proposes a mathematical framework for loss landscapes and optimization in deep neural networks.
Predicting epidemic dynamics is of great value in understanding and controlling diffusion processes, such as infectious disease spread and information propagation. This task is intractable, especially when surveillance resources are very limited. To address the challenge, we study the problem of active surveillance, i.…
Uniform bounds derived for fully non-linear equations.
Stability of recurrent models is closely linked with trainability, generalizability and in some applications, safety. Methods that train stable recurrent neural networks, however, do so at a significant cost to expressibility. We propose an implicit model structure that allows for a convex parametrization of stable mod…