# Research

## Interests

I am interested in just about any machine learning problem that is somewhat theoretical in nature. While I find probability and statistics fascinating in their own right, my research thus far has focused on applying tools and methods from these fields to get a better understanding of what we can guarantee about machine learning algorithms.

## Current Projects

Currently I am working on algorithms for active and semi-supervised learning.

## Publications

### Conference and Journal Papers

• A New Perspective on Learning Linear Separators with Large $$L_q L_p$$ Margins paper supp
M.-F. Balcan and C. Berlind
Conference on Artificial Intelligence and Statistics (AISTATS), 2014, to appear.
• Efficient Semi-supervised and Active Learning of Disjunctions paper supp
M.-F. Balcan, C. Berlind, S. Ehrlich, and Y. Liang
International Conference on Machine Learning (ICML), 2013.

### Workshop Contributions

• On Learning Linear Separators with Large $$L_\infty L_1$$ Margins poster
M.-F. Balcan and C. Berlind
Workshop on Learning Faster from Easy Data
Advances in Neural Information Processing Systems (NIPS), 2013.