arXiv research

New framework for analyzing games with multi-dimensional singular controls and non-linear jumps.

Broadens Jourdain and Martini's method to non-linear stochastic processes.

Non-linear control rules improve smart inverter performance in fluctuating grids.

New proof of generalized Chow-Rashevskii theorem for non-linear systems.

This research evaluates learning models for bionic robots, focusing on transfer function identification.

Neural ODEs control graph dynamics with low energy feedback.

New methods prove controllability of non-linear systems, extending classical results.

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…

New method disentangles perceptual uncertainty and behavioral costs in partially observable systems.

Paper tackles robust control of SDEs with ambiguity, proving value function existence and applying to investment problems.

Unified theory of $θ$-expectations derived from chaotic dynamics.

The paper analyzes deep neural networks using control theory to set a time limit for their convergence.

Paper uses non-linear dimension reduction for better economic forecasting.

Agent optimizes perpetual contract liquidation with transaction costs and risk.

Two proofs of Kalman Theorem using flows of vector fields.

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…

We solve a complex Bayesian control problem with novel methods.

Deep neural nets solve complex stochastic control problems.

Safe RL in linear systems achieves $\sqrt{T}$-regret.

Paper models non-linear dynamics from time series data.

We study an optimal control problem related to swing option pricing in a general non-Markovian setting in continuous time. As a main result we show that the value process solves a first-order non-linear backward stochastic partial differential equation. Based on this result we can characterize the set of optimal contro…

We study a multiplicative transient price impact model for an illiquid financial market, where trading causes price impact which is multiplicative in relation to the current price, transient over time with finite rate of resilience, and non-linear in the order size. We construct explicit solutions for the optimal contr…

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 …

In the two parts of this paper we solve a problem of De Rham, proving that Reidemeister torsion invariants determine topological equivalence of linear G-representations, for G a finite cyclic group. Methods in controlled K-theory and surgery theory are developed to establish, and effectively calculate, a necessary and …

Study optimal execution in financial markets with constraints.

This work establishes safe reinforcement learning for LQR with nonlinear baselines.

Reconstructing signature features from randomized vector fields in differential equations.

CRCCA framework improves non-linear CCA with compressed representations.

Reliable diagnosis of depressive disorder is essential for both optimal treatment and prevention of fatal outcomes. In this study, we aimed to elucidate the effectiveness of two non-linear measures, Higuchi Fractal Dimension (HFD) and Sample Entropy (SampEn), in detecting depressive disorders when applied on EEG. HFD a…

Improved Kalman filter for non-linear, non-Gaussian data.

Sharp L∞ estimates for fully non-linear elliptic equations on compact complex manifolds.

Proposes HSIC-Lasso for selective inference in non-linear data.

New framework for online control in evolving populations.

Improved control approach for correlated bandits with better performance.

DiffKnock improves feature selection in neural networks with complex dependencies and non-linear associations.

We prove continuity of a controlled SDE solution in Skorokhod's $M_1$ and $J_1$ topologies and also uniformly, in probability, as a non-linear functional of the control strategy. The functional comes from a finance problem to model price impact of a large investor in an illiquid market. We show that $M_1$-continuity is…

In this paper we consider a problem of searching a space of predictive models for a given training data set. We propose an iterative procedure for deriving a sequence of improving models and a corresponding sequence of sets of non-linear features on the original input space. After a finite number of iterations N, the n…

Hybrid controller combines model-based and policy-based reinforcement learning.

BAVART model combines VAR and BART for non-linear forecasting.

Novel approach for creating interpretable classifiers using bilevel optimization of split-rules in NLDTs.

A decentralized MARL algorithm for value propagation in non-linear settings.

Improved bounds for non-linear SA with fast convergence.

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…

This paper compares expected and distributional reinforcement learning methods.

Paper uses deep reinforcement learning for adaptive emergency control of power systems.

Introduces TDRC to balance TD's ease and soundness.

New method guides pretrained diffusion models without additional training.

Agent hedges non-tradable risk with traded asset, accounting for cross-impact and risk aversion.