no code implementations • 9 Dec 2019 • Shouvik Mani, Mehdi Maasoumy, Sina Pakazad, Henrik Ohlsson
As an alternative to standard regularization techniques, we propose Distance Metric Learning Regularization (DMLreg), an approach for eliciting prior knowledge from domain experts and integrating that knowledge into a regularized linear model.
no code implementations • 24 Feb 2014 • Fabien Lauer, Henrik Ohlsson
This paper deals with sparse phase retrieval, i. e., the problem of estimating a vector from quadratic measurements under the assumption that few components are nonzero.
no code implementations • 7 Dec 2013 • Dorsa Sadigh, Henrik Ohlsson, S. Shankar Sastry, Sanjit A. Seshia
As in robust PCA, it can be problematic to find a suitable regularization parameter.
no code implementations • 22 Nov 2013 • Fabien Lauer, Henrik Ohlsson
In particular, we show how these solutions can be recovered from group-sparse solutions of a derived system of linear equations.
no code implementations • 20 Sep 2013 • Henrik Ohlsson, Tianshi Chen, Sina Khoshfetrat Pakazad, Lennart Ljung, S. Shankar Sastry
The number of hypothesis grows rapidly with the number of systems and approximate solutions become a necessity for any problems of practical interests.
no code implementations • 20 Mar 2013 • Henrik Ohlsson, Yonina C. Eldar, Allen Y. Yang, S. Shankar Sastry
The problem is of great importance in many applications and is typically solved by maximizing the cross-correlation between the two signals.
no code implementations • NeurIPS 2012 • Henrik Ohlsson, Allen Yang, Roy Dong, Shankar Sastry
This paper presents a novel extension of CS to the phase retrieval problem, where intensity measurements of a linear system are used to recover a complex sparse signal.
1 code implementation • 10 Jun 2010 • Marc Peter Deisenroth, Henrik Ohlsson
We present a general probabilistic perspective on Gaussian filtering and smoothing.