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07/07/2023
Programmable Synthetic Tabular Data Generation
Large amounts of tabular data remain underutilized due to privacy, data ...
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10/13/2022
FARE: Provably Fair Representation Learning
Fair representation learning (FRL) is a popular class of methods aiming ...
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10/04/2022
Data Leakage in Tabular Federated Learning
While federated learning (FL) promises to preserve privacy in distribute...
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06/24/2022
Data Leakage in Federated Averaging
Recent attacks have shown that user data can be recovered from FedSGD up...
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02/17/2022
LAMP: Extracting Text from Gradients with Language Model Priors
Recent work shows that sensitive user data can be reconstructed from gra...
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11/26/2021
Latent Space Smoothing for Individually Fair Representations
Fair representation learning encodes user data to ensure fairness and ut...
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11/08/2021
Bayesian Framework for Gradient Leakage
Federated learning is an established method for training machine learnin...
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06/10/2021
Fair Normalizing Flows
Fair representation learning is an attractive approach that promises fai...
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03/30/2021
Robustness Certification for Point Cloud Models
The use of deep 3D point cloud models in safety-critical applications, s...
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02/12/2021
Certified Defenses: Why Tighter Relaxations May Hurt Training?
Certified defenses based on convex relaxations are an established techni...
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09/19/2020
Efficient Certification of Spatial Robustness
Recent work has exposed the vulnerability of computer vision models to s...
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05/27/2020
Fast and Effective Robustness Certification for Recurrent Neural Networks
We present a precise and scalable verifier for recurrent neural networks...
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02/24/2020