220 citations · 2.3k across the 62 of their papers we have counts for
7 papers · 1 filter
Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks
Patrick H. Chen, Si Si, Sanjiv Kumar +2
Neural language models have been widely used in various NLP tasks, including machine translation, next word prediction and conversational agents. However, it is challenging to depl…
Stochastic Negative Mining for Learning with Large Output Spaces
Sashank J. Reddi, Satyen Kale, Felix Yu +3
We consider the problem of retrieving the most relevant labels for a given input when the size of the output space is very large. Retrieval methods are modeled as set-valued classi…
Privacy and Utility Tradeoff in Approximate Differential Privacy
Quan Geng, Wei Ding, Ruiqi Guo +1
We characterize the minimum noise amplitude and power for noise-adding mechanisms in -differential privacy for single real-valued query function. We derive new lower bounds…
Optimal Noise-Adding Mechanism in Additive Differential Privacy
Quan Geng, Wei Ding, Ruiqi Guo +1
We derive the optimal -differentially private query-output independent noise-adding mechanism for single real-valued query function under a general cost-minimization framew…
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi +5
Linear encoding of sparse vectors is widely popular, but is commonly data-independent -- missing any possible extra (but a priori unknown) structure beyond sparsity. In this paper…
cpSGD: Communication-efficient and differentially-private distributed SGD
Naman Agarwal, Ananda Theertha Suresh, Felix Yu +2
Distributed stochastic gradient descent is an important subroutine in distributed learning. A setting of particular interest is when the clients are mobile devices, where two impor…