4 citations · 7 across the 28 of their papers we have counts for
6 papers · 1 filter
Active Learning with Low-Rank Structure for Data Selection
Vincent Cohen-Addad, Sasidhar Kunapuli, Vahab Mirrokni +3
In the data selection problem, the objective is to choose a small, representative subset of data that can be used to efficiently train a machine learning model. Sener and Savarese…
Better Bounds for the Distributed Experts Problem
David P. Woodruff, Samson Zhou
In this paper, we study the distributed experts problem, where experts are distributed across servers for timesteps. The loss of each expert at each time is the $\e…
Private Training & Data Generation by Clustering Embeddings
Felix Zhou, Samson Zhou, Vahab Mirrokni +2
Deep neural networks often use large, high-quality datasets to achieve high performance on many machine learning tasks. When training involves potentially sensitive data, this proc…
On Socially Fair Low-Rank Approximation and Column Subset Selection
Zhao Song, Ali Vakilian, David P. Woodruff +1
Low-rank approximation and column subset selection are two fundamental and related problems that are applied across a wealth of machine learning applications. In this paper, we stu…
Learning a Latent Simplex in Input-Sparsity Time
Ainesh Bakshi, Chiranjib Bhattacharyya, Ravi Kannan +2
We consider the problem of learning a latent -vertex simplex , given access to , which can be viewed as a data matrix with …
Data-Independent Structured Pruning of Neural Networks via Coresets
Ben Mussay, Daniel Feldman, Samson Zhou +2
Model compression is crucial for deployment of neural networks on devices with limited computational and memory resources. Many different methods show comparable accuracy of the co…