56 citations · 130 across the 13 of their papers we have counts for
13 papers
Large-Scale Public Data Improves Differentially Private Image Generation Quality
Ruihan Wu, Chuan Guo, Kamalika Chaudhuri
Public data has been frequently used to improve the privacy-accuracy trade-off of differentially private machine learning, but prior work largely assumes that this data come from t…
Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning
Ruihan Wu, Xiangyu Chen, Chuan Guo +1
Gradient inversion attack enables recovery of training samples from model gradients in federated learning (FL), and constitutes a serious threat to data privacy. To mitigate this v…
Differentially Private Multi-Party Data Release for Linear Regression
Ruihan Wu, Xin Yang, Yuanshun Yao +4
Differentially Private (DP) data release is a promising technique to disseminate data without compromising the privacy of data subjects. However the majority of prior work has focu…
Does Label Differential Privacy Prevent Label Inference Attacks?
Ruihan Wu, Jin Peng Zhou, Kilian Q. Weinberger +1
Label differential privacy (label-DP) is a popular framework for training private ML models on datasets with public features and sensitive private labels. Despite its rigorous priv…
Hefei light source storage ring bunch by bunch 3D position measuring system is introduced and a preliminary study is shown
Ping Lu, Leilei Tang, Baogen Sun +4
The 4 electrode signals of the beam position detector(BPM) of the STORAGE ring of HEFEI Light Source are directly connected to the domestic oscilloscope with 12bit resolution, 10Gs…
Online Adaptation to Label Distribution Shift
Ruihan Wu, Chuan Guo, Yi Su +1
Machine learning models often encounter distribution shifts when deployed in the real world. In this paper, we focus on adaptation to label distribution shift in the online setting…
Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems
Ruihan Wu, Chuan Guo, Awni Hannun +1
Machine-learning systems such as self-driving cars or virtual assistants are composed of a large number of machine-learning models that recognize image content, transcribe speech,…
Making Paper Reviewing Robust to Bid Manipulation Attacks
Ruihan Wu, Chuan Guo, Felix Wu +3
Most computer science conferences rely on paper bidding to assign reviewers to papers. Although paper bidding enables high-quality assignments in days of unprecedented submission n…
Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data
Cole Miles, Annabelle Bohrdt, Ruihan Wu +7
Machine learning models are a powerful theoretical tool for analyzing data from quantum simulators, in which results of experiments are sets of snapshots of many-body states. Recen…
On Hiding Neural Networks Inside Neural Networks
Chuan Guo, Ruihan Wu, Kilian Q. Weinberger
Modern neural networks often contain significantly more parameters than the size of their training data. We show that this excess capacity provides an opportunity for embedding sec…
Product Kernel Interpolation for Scalable Gaussian Processes
Jacob R. Gardner, Geoff Pleiss, Ruihan Wu +2
Recent work shows that inference for Gaussian processes can be performed efficiently using iterative methods that rely only on matrix-vector multiplications (MVMs). Structured Kern…
Scalable Lattice Influence Maximization
Wei Chen, Ruihan Wu, Zheng Yu
Influence maximization is the task of finding k seed nodes in a social network such that the expected number of activated nodes in the network (under certain influence propagation…
Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes
Lunjia Hu, Ruihan Wu, Tianhong Li +1
In this work we study the quantitative relation between the recursive teaching dimension (RTD) and the VC dimension (VCD) of concept classes of finite sizes. The RTD of a concept c…