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10/19/2022
Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning
Gradient inversion attack enables recovery of training samples from mode...
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06/16/2022
Differentially Private Multi-Party Data Release for Linear Regression
Differentially Private (DP) data release is a promising technique to dis...
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02/25/2022
Does Label Differential Privacy Prevent Label Inference Attacks?
Label differential privacy (LDP) is a popular framework for training pri...
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07/09/2021
Online Adaptation to Label Distribution Shift
Machine learning models often encounter distribution shifts when deploye...
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03/22/2021
Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems
Machine-learning systems such as self-driving cars or virtual assistants...
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02/09/2021
Making Paper Reviewing Robust to Bid Manipulation Attacks
Most computer science conferences rely on paper bidding to assign review...
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11/06/2020
Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data
Machine learning models are a powerful theoretical tool for analyzing da...
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02/24/2020
TrojanNet: Embedding Hidden Trojan Horse Models in Neural Networks
The complexity of large-scale neural networks can lead to poor understan...
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02/24/2018
Product Kernel Interpolation for Scalable Gaussian Processes
Recent work shows that inference for Gaussian processes can be performed...
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02/13/2018
Scalable Influence Maximization with General Marketing Strategies
In this paper, we study scalable algorithms for influence maximization w...
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02/18/2017
