no code implementations • 23 Feb 2016 • Maria Florina Balcan, Simon S. Du, Yining Wang, Adams Wei Yu
We consider the noisy power method algorithm, which has wide applications in machine learning and statistics, especially those related to principal component analysis (PCA) under resource (communication, memory or privacy) constraints.
no code implementations • 15 Dec 2015 • Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria Florina Balcan, Alex Smola
In distributed machine learning, data is dispatched to multiple machines for processing.
no code implementations • 10 Nov 2015 • Maria Florina Balcan, Travis Dick, Yishay Mansour
We present a new perspective on the popular multi-class algorithmic techniques of one-vs-all and error correcting output codes.
no code implementations • 8 Dec 2013 • Nan Du, YIngyu Liang, Maria Florina Balcan, Le Song
The typical algorithmic problem in viral marketing aims to identify a set of influential users in a social network, who, when convinced to adopt a product, shall influence other users in the network and trigger a large cascade of adoptions.
no code implementations • 31 Jul 2013 • Pranjal Awasthi, Maria Florina Balcan, Philip M. Long
For malicious noise, where the adversary can corrupt both the label and the features, we provide a polynomial-time algorithm for learning linear separators in $\Re^d$ under isotropic log-concave distributions that can tolerate a nearly information-theoretically optimal noise rate of $\eta = \Omega(\epsilon)$.
no code implementations • 11 Jul 2013 • Maria Florina Balcan, Vitaly Feldman
These results combined with our generic conversion lead to the first computationally-efficient algorithms for actively learning some of these concept classes in the presence of random classification noise that provide exponential improvement in the dependence on the error $\epsilon$ over their passive counterparts.
no code implementations • NeurIPS 2013 • Maria Florina Balcan, Steven Ehrlich, YIngyu Liang
We provide a distributed method for constructing a global coreset which improves over the previous methods by reducing the communication complexity, and which works over general communication topologies.
no code implementations • 6 Nov 2012 • Maria Florina Balcan, Philip M. Long
We provide new results concerning label efficient, polynomial time, passive and active learning of linear separators.
no code implementations • 5 Dec 2011 • Maria Florina Balcan, YIngyu Liang
For $k$-median, a center-based objective of special interest, we additionally give algorithms for a more relaxed assumption in which we allow the optimal solution to change in a small $\epsilon$ fraction of the points after perturbation.