9 citations · 14 across the 5 of their papers we have counts for
13 papers
Learning Models of Model Predictive Controllers using Gradient Data
Rebecka Winqvist, Arun Venkitaraman, Bo Wahlberg
This paper investigates controller identification given data from a Model Predictive Controller (MPC) with constraints. We propose an approach for learning MPC that explicitly uses…
Task-similarity Aware Meta-learning through Nonparametric Kernel Regression
Arun Venkitaraman, Anders Hansson, Bo Wahlberg
This paper investigates the use of nonparametric kernel-regression to obtain a tasksimilarity aware meta-learning algorithm. Our hypothesis is that the use of tasksimilarity helps…
Predictive Analysis of COVID-19 Time-series Data from Johns Hopkins University
Alireza M. Javid, Xinyue Liang, Arun Venkitaraman +1
We provide a predictive analysis of the spread of COVID-19, also known as SARS-CoV-2, using the dataset made publicly available online by the Johns Hopkins University. Our main obj…
On Training and Evaluation of Neural Network Approaches for Model Predictive Control
Rebecka Winqvist, Arun Venkitaraman, Bo Wahlberg
The contribution of this paper is a framework for training and evaluation of Model Predictive Control (MPC) implemented using constrained neural networks. Recent studies have propo…
High-dimensional Neural Feature Design for Layer-wise Reduction of Training Cost
Alireza M. Javid, Arun Venkitaraman, Mikael Skoglund +1
We design a ReLU-based multilayer neural network by mapping the feature vectors to a higher dimensional space in every layer. We design the weight matrices in every layer to ensure…
Learning sparse linear dynamic networks in a hyper-parameter free setting
Arun Venkitaraman, Håkan Hjalmarsson, Bo Wahlberg
We address the issue of estimating the topology and dynamics of sparse linear dynamic networks in a hyperparameter-free setting. We propose a method to estimate the network dynamic…