My main research interest is nonlinear inference, especially within the context of dynamical systems, solved using probabilistic models and algorithms. In terms of scientific fields, my research is situated somewhere on the intersection between the fields of machine learning, signal processing and automatic control. My aim is to pursue both basic and applied research, where the latter is typically carried out in close collaboration with industry. A brief overview of my research is available here and my publications are available here.
Recent research results/news
May 12, 2015: Data-efficient learning in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. In some recent results that was just accepted to the deep learning workshop at ICML this summer we we consider one instance of this challenge, the so-called pixels-to-torques problem, where an agent must learn a closed-loop control policy from pixel information only. We introduce a data-efficient, model-based reinforcement learning algorithm that learns such a closed-loop policy directly from pixel information.
Niklas Wahlström, Thomas B. Schön and Marc Peter Deisenroth. From pixels to torques: policy learning with deep dynamical models. Deep Learning Workshop at the International Conference on Machine Learning (ICML), Lille, France, July 2015. (accepted for publication) [arXiv]
April 27, 2015: Our new sequential Monte Carlo (SMC) construction enabling inference in high(er) dimensional models has been accepted to the International Conference on Machine Learning (ICML), which will be held in Lille (France) in July. The new construction mimics fully adapted proposals for latent spaces and structures in high dimensional models. The key is a nested coupling of multiple SMC samplers and backward simulators.
Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön. Nested sequential Monte Carlo methods. In Proceedings of the 32nd International Conference on Machine Learning (ICML), Lille, France, July, 2015. (accepted for publication) [pdf] [arXiv]
April 10, 2015: Our tutorial paper Sequential Monte Carlo Methods for System Identification is now available as a pre-print on arXiv. In this paper we describe different approaches (both Bayesian and frequentist) to estimate parameters in general state space models, also know as hidden Markov models. The SMC approach is motivated by its ability to deal with the intractability of nonlinear and non-Gaussian state space models.
January 29, 2015: The details concerning the one-day tutorial that I will give on nonlinear system identification using sequential Monte Carlo (SMC) methods are now available. The tutorial is given as a part of the Summer School on Foundations and advances in stochastic filtering, which will be held in Barcelona, June 22-26, 2015.
January 26, 2015: The sequential Monte Carlo (SMC) workshop will be held in Paris in August. More information is available from here, where you can also register.
January 23, 2015: The website for the Uppsala CARS (Camera-based Autonomous Racing System) project is now available here! If you are student here in Uppsala interested in working on this project, let us know since we are currently in the process of assembling a new team. Some ideas for future projects are available here, but own ideas are of course also welcome.
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