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Found 9 papers, 0 papers with code
An Improved Gap-Dependency Analysis of the Noisy Power Method
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.
Data Driven Resource Allocation for Distributed Learning
In distributed machine learning, data is dispatched to multiple machines for processing.
Label Efficient Learning by Exploiting Multi-class Output Codes
We present a new perspective on the popular multi-class algorithmic techniques of one-vs-all and error correcting output codes.
Budgeted Influence Maximization for Multiple Products
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.
The Power of Localization for Efficiently Learning Linear Separators with Noise
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 $\eta = \Omega(\epsilon)$.
Statistical Active Learning Algorithms for Noise Tolerance and Differential Privacy
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.
Distributed k-Means and k-Median Clustering on General Topologies
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.
Active and passive learning of linear separators under log-concave distributions
We provide new results concerning label efficient, polynomial time, passive and active learning of linear separators.
Clustering under Perturbation Resilience
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.
