5 citations · 15 across the 5 of their papers we have counts for
5 papers
Wasserstein Adversarial Transformer for Cloud Workload Prediction
Shivani Arbat, Vinodh Kumaran Jayakumar, Jaewoo Lee +2
Predictive Virtual Machine (VM) auto-scaling is a promising technique to optimize cloud applications operating costs and performance. Understanding the job arrival rate is crucial…
Differentially Private Deep Learning with Direct Feedback Alignment
Jaewoo Lee, Daniel Kifer
Standard methods for differentially private training of deep neural networks replace back-propagated mini-batch gradients with biased and noisy approximations to the gradient. Thes…
Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping
Jaewoo Lee, Daniel Kifer
Recent work on Renyi Differential Privacy has shown the feasibility of applying differential privacy to deep learning tasks. Despite their promise, however, differentially private…
Stochastic Adaptive Line Search for Differentially Private Optimization
Chen Chen, Jaewoo Lee
The performance of private gradient-based optimization algorithms is highly dependent on the choice of step size (or learning rate) which often requires non-trivial amount of tunin…
Building a Collaborative Phone Blacklisting System with Local Differential Privacy
Daniele Ucci, Roberto Perdisci, Jaewoo Lee +1
Spam phone calls have been rapidly growing from nuisance to an increasingly effective scam delivery tool. To counter this increasingly successful attack vector, a number of commerc…