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.
Controller seeks informative system observations to predict nonlinear dynamics.
New model optimizes portfolios over multiple periods using predictive control.
New model predicts optimal control for restless bandit problems.
Combines Gaussian processes and polynomial chaos for stochastic control.
Review of integrating Bayesian methods with neural network-based MPC.
Researchers develop a method to control nonlinear systems with Koopman operator regression.
MPC outperforms reactive budgeting in non-stationary return environments.
Develops a learning model predictive controller for competitive racing.
A new approach optimizes weights in DLP for better risk-adjusted performance.
In this paper, we show the implementation of deep neural networks applied in process control. In our approach, we based the training of the neural network on model predictive control. Model predictive control is popular for its ability to be tuned by the weighting matrices and by the fact that it respects the constrain…
Paper proposes a new model for better engine control.
In this paper, we propose a decision making algorithm intended for automated vehicles that negotiate with other possibly non-automated vehicles in intersections. The decision algorithm is separated into two parts: a high-level decision module based on reinforcement learning, and a low-level planning module based on mod…
In this paper, we introduce an actor-critic algorithm called Deep Value Model Predictive Control (DMPC), which combines model-based trajectory optimization with value function estimation. The DMPC actor is a Model Predictive Control (MPC) optimizer with an objective function defined in terms of a value function estimat…
Hybridizes CEM and gradient descent for efficient model-predictive control.
Method predicts hardware resource usage by control software with guaranteed linear convergence.
Robo-advisors use MPC to create dynamic investment strategies.
This paper develops a framework for training and evaluating neural networks for MPC.
We propose the use of Bayesian networks, which provide both a mean value and an uncertainty estimate as output, to enhance the safety of learned control policies under circumstances in which a test-time input differs significantly from the training set. Our algorithm combines reinforcement learning and end-to-end imita…
Bayesian optimisation tackles stochastic MPC hyper-parameter tuning.
A new active learning method for Gaussian process models.
MPC framework reduces execution costs and schedule deviations in trading.
Paper uses imitation learning to create efficient insulin policies from MPC demonstrations.
Solves Merton's investment-consumption problem with certainty equivalent approach.
This work presents an explicit-implicit procedure to compute a model predictive control (MPC) law with guarantees on recursive feasibility and asymptotic stability. The approach combines an offline-trained fully-connected neural network with an online primal active set solver. The neural network provides a control inpu…
EHR-MPC optimizes sepsis treatment using digital twins and inference-time control.
We present a unified method, based on convex optimization, for managing the power produced and consumed by a network of devices over time. We start with the simple setting of optimizing power flows in a static network, and then proceed to the case of optimizing dynamic power flows, i.e., power flows that change with ti…
Model-free Reinforcement Learning (RL) works well when experience can be collected cheaply and model-based RL is effective when system dynamics can be modeled accurately. However, both assumptions can be violated in real world problems such as robotics, where querying the system can be expensive and real-world dynamics…
We propose directed time series regression, a new approach to estimating parameters of time-series models for use in certainty equivalent model predictive control. The approach combines merits of least squares regression and empirical optimization. Through a computational study involving a stochastic version of a well …
We seek a discussion about the most suitable feedback control structure for stock trading under the consideration of proportional transaction costs. Suitability refers to robustness and performance capability. Both are tested by considering different one-step ahead prediction qualities, including the ideal case, correc…
The control of complex systems is of critical importance in many branches of science, engineering, and industry. Controlling an unsteady fluid flow is particularly important, as flow control is a key enabler for technologies in energy (e.g., wind, tidal, and combustion), transportation (e.g., planes, trains, and automo…
In this work, we consider the optimal portfolio selection problem under hard constraints on trading volume amounts when the dynamics of the risky asset returns are governed by a discrete-time approximation of the Markov-modulated geometric Brownian motion. The states of Markov chain are interpreted as the states of an …
New method handles robust and adaptive control of linear systems with non-convex costs.
Neural Laplace Control tackles offline RL for continuous-time delayed systems with irregular observations.
The paper explores how ReLU DNNs can represent MPC policies and vice versa.
Trial-and-error based reinforcement learning (RL) has seen rapid advancements in recent times, especially with the advent of deep neural networks. However, the majority of autonomous RL algorithms require a large number of interactions with the environment. A large number of interactions may be impractical in many real…
New algorithm optimizes decision-making for complex systems with varying parameters.
Optimal trading strategy using LQR framework with price mean-reversion.
Model predictive control (MPC) has become one of the well-established modern control methods for three-phase inverters with an output LC filter, where a high-quality voltage with low total harmonic distortion (THD) is needed. Although it is an intuitive controller, easy to understand and implement, it has the significa…
We study data poisoning attacks in the online setting where training items arrive sequentially, and the attacker may perturb the current item to manipulate online learning. Importantly, the attacker has no knowledge of future training items nor the data generating distribution. We formulate online data poisoning attack…
We extend Bayes' theorem for upper probabilities considering likelihood uncertainty.
Optimal control of reserve assets for stablecoins to maintain peg stability.
Proposes a recursive MPC scheme with probabilistic safety guarantees for uncertain dynamic systems.
We introduce and discuss optimal control strategies for kinetic models for wealth distribution in a simple market economy, acting to minimize the variance of the wealth density among the population. Our analysis is based on a finite time horizon approximation, or model predictive control, of the corresponding control p…
In science and medicine, model interpretations may be reported as discoveries of natural phenomena or used to guide patient treatments. In such high-stakes tasks, false discoveries may lead investigators astray. These applications would therefore benefit from control over the finite-sample error rate of interpretations…
Model-based reinforcement learning (MBRL) with model-predictive control or online planning has shown great potential for locomotion control tasks in terms of both sample efficiency and asymptotic performance. Despite their initial successes, the existing planning methods search from candidate sequences randomly generat…
Paper uses deep reinforcement learning for better control of rocket engines during start-up phases.
We present foundations for using Model Predictive Control (MPC) as a differentiable policy class for reinforcement learning in continuous state and action spaces. This provides one way of leveraging and combining the advantages of model-free and model-based approaches. Specifically, we differentiate through MPC by usin…
Robot-assisted dressing offers an opportunity to benefit the lives of many people with disabilities, such as some older adults. However, robots currently lack common sense about the physical implications of their actions on people. The physical implications of dressing are complicated by non-rigid garments, which can r…