Search Results for author:
Found 8 papers, 2 papers with code
STELLA: Continual Audio-Video Pre-training with Spatio-Temporal Localized Alignment
Continuously learning a variety of audio-video semantics over time is crucial for audio-related reasoning tasks in our ever-evolving world.
Wasserstein Adversarial Transformer for Cloud Workload Prediction
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.
Differentially Private Deep Learning with Direct Feedback Alignment
Standard methods for differentially private training of deep neural networks replace back-propagated mini-batch gradients with biased and noisy approximations to the gradient.
Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping
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.
Stochastic Adaptive Line Search for Differentially Private Optimization
In this paper, we introduce a stochastic variant of classic backtracking line search algorithm that satisfies R\'enyi differential privacy.
Renyi Differentially Private ADMM for Non-Smooth Regularized Optimization
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).
Concentrated Differentially Private Gradient Descent with Adaptive per-Iteration Privacy Budget
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.
Differentially Private Confidence Intervals for Empirical Risk Minimization
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.
