activity
20132021
most citedRobust exploration in linear quadratic reinforcement learning

4 citations · 4 across the 4 of their papers we have counts for

collaborators

5 papers

eess.SY2021

Non-causal regularized least-squares for continuous-time system identification with band-limited input excitations

Rodrigo A. González, Cristian R. Rojas, Håkan Hjalmarsson

In continuous-time system identification, the intersample behavior of the input signal is known to play a crucial role in the performance of estimation methods. One common input be…

stat.ML2019

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…

math.OC20194 cited

Robust exploration in linear quadratic reinforcement learning

Jack Umenberger, Mina Ferizbegovic, Thomas B. Schön +1

This paper concerns the problem of learning control policies for an unknown linear dynamical system to minimize a quadratic cost function. We present a method, based on convex opti…

cs.IT2013

On the Design of Channel Estimators for given Signal Estimators and Detectors

Dimitrios Katselis, Cristian R. Rojas, Håkan Hjalmarsson +2

The fundamental task of a digital receiver is to decide the transmitted symbols in the best possible way, i.e., with respect to an appropriately defined performance metric. Example…

cs.IT2013

Training Sequence Design for MIMO Channels: An Application-Oriented Approach

Dimitrios Katselis, Cristian R. Rojas, Mats Bengtsson +5

In this paper, the problem of training optimization for estimating a multiple-input multiple-output (MIMO) flat fading channel in the presence of spatially and temporally correlate…