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Found 7 papers, 1 papers with code
Recovery Guarantees for Distributed-OMP
We study distributed schemes for high-dimensional sparse linear regression, based on orthogonal matching pursuit (OMP).
Distributed Sparse Normal Means Estimation with Sublinear Communication
We assume there are $M$ machines, each holding $d$-dimensional observations of a $K$-sparse vector $\mu$ corrupted by additive Gaussian noise.
Coresets for Ordered Weighted Clustering
We design coresets for Ordered k-Median, a generalization of classical clustering problems such as k-Median and k-Center, that offers a more flexible data analysis, like easily combining multiple objectives (e. g., to increase fairness or for Pareto optimization).
Data Structures and Algorithms
Do semidefinite relaxations solve sparse PCA up to the information limit?
In fact, we conjecture that in the single-spike model, no computationally-efficient algorithm can recover a spike of $\ell_0$-sparsity $k\geq\Omega(\sqrt{n})$.
Efficient Classification for Metric Data
We design a new algorithm for classification in general metric spaces, whose runtime and accuracy depend on the doubling dimension of the data points, and can thus achieve superior classification performance in many common scenarios.
Adaptive Metric Dimensionality Reduction
We study adaptive data-dependent dimensionality reduction in the context of supervised learning in general metric spaces.
Efficient Regression in Metric Spaces via Approximate Lipschitz Extension
We present a framework for performing efficient regression in general metric spaces.
