activity
20152020
most citedScalable Greedy Feature Selection via Weak Submodularity

28 citations · 40 across the 5 of their papers we have counts for

collaborators

7 papers

cs.AR2020

Achieving Multi-Port Memory Performance on Single-Port Memory with Coding Techniques

Hardik Jain, Matthew Edwards, Ethan Elenberg +2

Many performance critical systems today must rely on performance enhancements, such as multi-port memories, to keep up with the increasing demand of memory-access capacity. However…

cs.LG2020

Identifying Mislabeled Data using the Area Under the Margin Ranking

Geoff Pleiss, Tianyi Zhang, Ethan R. Elenberg +1

Not all data in a typical training set help with generalization; some samples can be overly ambiguous or outrightly mislabeled. This paper introduces a new method to identify such…

cs.LG2019

Metric Learning for Dynamic Text Classification

Jeremy Wohlwend, Ethan R. Elenberg, Samuel Altschul +2

Traditional text classifiers are limited to predicting over a fixed set of labels. However, in many real-world applications the label set is frequently changing. For example, in in…

stat.ML2018

Importance Weighted Generative Networks

Maurice Diesendruck, Ethan R. Elenberg, Rajat Sen +3

Deep generative networks can simulate from a complex target distribution, by minimizing a loss with respect to samples from that distribution. However, often we do not have direct…

stat.ML201728 cited

Scalable Greedy Feature Selection via Weak Submodularity

Rajiv Khanna, Ethan Elenberg, Alexandros G. Dimakis +2

Greedy algorithms are widely used for problems in machine learning such as feature selection and set function optimization. Unfortunately, for large datasets, the running time of e…

stat.ML20175 cited

On Approximation Guarantees for Greedy Low Rank Optimization

Rajiv Khanna, Ethan Elenberg, Alexandros G. Dimakis +1

We provide new approximation guarantees for greedy low rank matrix estimation under standard assumptions of restricted strong convexity and smoothness. Our novel analysis also unco…