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Mike Chung

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2 papers

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NeurIPS Conference 2012 Conference Paper

Structured Learning of Gaussian Graphical Models

Karthik Mohan
Mike Chung
Seungyeop Han
Daniela Witten
Su-In Lee
Maryam Fazel

We consider estimation of multiple high-dimensional Gaussian graphical models corresponding to a single set of nodes under several distinct conditions. We assume that most aspects of the networks are shared, but that there are some structured differences between them. Specifically, the network differences are generated from node perturbations: a few nodes are perturbed across networks, and most or all edges stemming from such nodes differ between networks. This corresponds to a simple model for the mechanism underlying many cancers, in which the gene regulatory network is disrupted due to the aberrant activity of a few specific genes. We propose to solve this problem using the structured joint graphical lasso, a convex optimization problem that is based upon the use of a novel symmetric overlap norm penalty, which we solve using an alternating directions method of multipliers algorithm. Our proposal is illustrated on synthetic data and on an application to brain cancer gene expression data.

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IJCAI Conference 2011 Conference Paper

A Hierarchical Architecture for Adaptive Brain-Computer Interfacing

Mike Chung
Willy Cheung
Reinhold Scherer
Rajesh P. N. Rao

Brain-computer interfaces (BCIs) allow a user to directly control devices such as cursors and robots using brain signals. Non-invasive BCIs, e. g. , those based on electroencephalographic (EEG) signals recorded from the scalp, suffer from low signal-to-noise ratio which limits the bandwidth of control. Invasive BCIs allow fine-grained control but can leave users exhausted since control is typically exerted on a moment-by-moment basis. In this paper, we address these problems by proposing a new adaptive hierarchical architecture for brain-computer interfacing. The approach allows a user to teach the BCI new skills on-the-fly; these learned skills are later invoked directly as high-level commands, relieving the user of tedious low-level control. We report results from four subjects who used a hierarchical EEG-based BCI to successfully train and control a humanoid robot in a virtual home environment. Gaussian processes were used for learning high-level commands, allowing a BCI to switch between autonomous and user-guided modes based on the current estimate of uncertainty. We also report the first instance of multi-tasking in a BCI, involving simultaneous control of two different devices by a single user. Our results suggest that hierarchical BCIs can provide a flexible and robust way of controlling complex robotic devices in real-world environments.

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