47 citations · 78 across the 9 of their papers we have counts for
8 papers · 1 filter
Selective Pre-training for Private Fine-tuning
Da Yu, Sivakanth Gopi, Janardhan Kulkarni +5
Text prediction models, when used in applications like email clients or word processors, must protect user data privacy and adhere to model size constraints. These constraints are…
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent
Da Yu, Gautam Kamath, Janardhan Kulkarni +3
Differentially private stochastic gradient descent (DP-SGD) is the workhorse algorithm for recent advances in private deep learning. It provides a single privacy guarantee to all d…
Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANs
Bowen Tian, Qinliang Su, Jian Yin
The goal of anomaly detection is to identify anomalous samples from normal ones. In this paper, a small number of anomalies are assumed to be available at the training stage, but t…
Availability Attacks Create Shortcuts
Da Yu, Huishuai Zhang, Wei Chen +2
Availability attacks, which poison the training data with imperceptible perturbations, can make the data \emph{not exploitable} by machine learning algorithms so as to prevent unau…
Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer
Siyuan Liu, Yusong Wang, Tong Wang +6
The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential ro…
Instance-wise Graph-based Framework for Multivariate Time Series Forecasting
Wentao Xu, Weiqing Liu, Jiang Bian +2
The multivariate time series forecasting has attracted more and more attention because of its vital role in different fields in the real world, such as finance, traffic, and weathe…