[Colloquium] TTIC Talk: Han Liu, CMU

[Julia MacGlashan]

When:             *Tuesday, Feb 2 @ 11:00am*

Where:           * TTI-C Conference Room #526*, 6045 S Kenwood Ave


Who:              * **Han Liu*, CMU


Title:          *      **Nonparametric Learning in High Dimensions***



 Despite the high dimensionality and complexity of many modern datasets,
some problems have hidden structure that makes efficient statistical
inference feasible. Examples of these hidden structures include: additivity,
sparsity, low-dimensional manifold structure, smoothness, copula structure,
and conditional independence relations.

In this talk, I will describe efficient nonparametric learning algorithms
that exploit such hidden structures to overcome the curse of dimensionality.
These algorithms have strong theoretical guarantees and provide practical
methods for many fundamentally important learning problems, ranging from
unsupervised exploratory data analysis to supervised predictive modeling.

I will use two examples of high dimensional graph estimation and multi-task
regression to illustrate the principles of developing high dimensional
nonparametric methods. The theoretical results are presented in terms of
risk consistency, estimation consistency, and model selection consistency.
The practical performance of the algorithms is illustrated on genomics and
cognitive neuroscience examples and compared to state-of-the-art parametric
competitors.

This work is joint with John Lafferty and Larry Wasserman.

Host:              Nati Srebro, nati at ttic.edu*


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