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Found 4 papers, 1 papers with code
LocalNewton: Reducing Communication Bottleneck for Distributed Learning
To enhance practicability, we devise an adaptive scheme to choose L, and we show that this reduces the number of local iterations in worker machines between two model synchronizations as the training proceeds, successively refining the model quality at the master.
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method
The Column Subset Selection Problem (CSSP) and the Nystrom method are among the leading tools for constructing small low-rank approximations of large datasets in machine learning and scientific computing.
Sampling from a k-DPP without looking at all items
Determinantal point processes (DPPs) are a useful probabilistic model for selecting a small diverse subset out of a large collection of items, with applications in summarization, recommendation, stochastic optimization, experimental design and more.
The limits of squared Euclidean distance regularization
We conjecture that our hardness results hold for any training algorithm that is based on the squared Euclidean distance regularization (i. e. Back-propagation with the Weight Decay heuristic).
