1.1k citations · 2.4k across the 69 of their papers we have counts for
6 papers · 1 filter
ProxQuant: Quantized Neural Networks via Proximal Operators
Yu Bai, Yu-Xiang Wang, Edo Liberty
To make deep neural networks feasible in resource-constrained environments (such as mobile devices), it is beneficial to quantize models by using low-precision weights. One common…
Subsampled Rényi Differential Privacy and Analytical Moments Accountant
Yu-Xiang Wang, Borja Balle, Shiva Kasiviswanathan
We study the problem of subsampling in differential privacy (DP), a question that is the centerpiece behind many successful differentially private machine learning algorithms. Spec…
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
Borja Balle, Yu-Xiang Wang
The Gaussian mechanism is an essential building block used in multitude of differentially private data analysis algorithms. In this paper we revisit the Gaussian mechanism and show…
Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain
Yu-Xiang Wang
We revisit the problem of linear regression under a differential privacy constraint. By consolidating existing pieces in the literature, we clarify the correct dependence of the fe…
signSGD: Compressed Optimisation for Non-Convex Problems
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli +1
Training large neural networks requires distributing learning across multiple workers, where the cost of communicating gradients can be a significant bottleneck. signSGD alleviates…
Detecting and Correcting for Label Shift with Black Box Predictors
Zachary C. Lipton, Yu-Xiang Wang, Alex Smola
Faced with distribution shift between training and test set, we wish to detect and quantify the shift, and to correct our classifiers without test set labels. Motivated by medical…