no code implementations • 12 Oct 2023 • Jaewoo Lee, Jaehong Yoon, Wonjae Kim, Yunji Kim, Sung Ju Hwang
Continuously learning a variety of audio-video semantics over time is crucial for audio-related reasoning tasks in our ever-evolving world.
1 code implementation • 12 Mar 2022 • Shivani Arbat, Vinodh Kumaran Jayakumar, Jaewoo Lee, Wei Wang, In Kee Kim
Understanding the job arrival rate is crucial for accurately predicting future changes in cloud workloads and proactively provisioning and de-provisioning VMs for hosting the applications.
no code implementations • 8 Oct 2020 • 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.
no code implementations • 7 Sep 2020 • Jaewoo Lee, Daniel Kifer
The reason for this slowdown is a crucial privacy-related step called "per-example gradient clipping" whose naive implementation undoes the benefits of batch training with GPUs.
no code implementations • 18 Aug 2020 • Chen Chen, Jaewoo Lee
In this paper, we introduce a stochastic variant of classic backtracking line search algorithm that satisfies R\'enyi differential privacy.
no code implementations • 18 Sep 2019 • Chen Chen, Jaewoo Lee
In this paper we consider the problem of minimizing composite objective functions consisting of a convex differentiable loss function plus a non-smooth regularization term, such as $L_1$ norm or nuclear norm, under R\'enyi differential privacy (RDP).
1 code implementation • 28 Aug 2018 • Jaewoo Lee, Daniel Kifer
It outperforms prior algorithms for model fitting and is competitive with the state-of-the-art for $(\epsilon,\delta)$-differential privacy, a strictly weaker definition than zCDP.
no code implementations • 11 Apr 2018 • Yue Wang, Daniel Kifer, Jaewoo Lee
The process of data mining with differential privacy produces results that are affected by two types of noise: sampling noise due to data collection and privacy noise that is designed to prevent the reconstruction of sensitive information.