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06/07/2023
Privately generating tabular data using language models
Privately generating synthetic data from a table is an important brick o...
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10/24/2022
Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano
Differential privacy (DP) is by far the most widely accepted framework f...
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10/07/2022
TAN without a burn: Scaling Laws of DP-SGD
Differentially Private methods for training Deep Neural Networks (DNNs) ...
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10/06/2022
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning
Federated Learning (FL) is a setting for training machine learning model...
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02/15/2022
Defending against Reconstruction Attacks with Rényi Differential Privacy
Reconstruction attacks allow an adversary to regenerate data samples of ...
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12/17/2021
Watermarking Images in Self-Supervised Latent Spaces
We revisit watermarking techniques based on pre-trained deep networks, i...
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12/17/2021
Nearest neighbor search with compact codes: A decoder perspective
Modern approaches for fast retrieval of similar vectors on billion-scale...
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09/25/2021
Opacus: User-Friendly Differential Privacy Library in PyTorch
We introduce Opacus, a free, open-source PyTorch library for training de...
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04/15/2021
Gradient-based Adversarial Attacks against Text Transformers
We propose the first general-purpose gradient-based attack against trans...
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03/31/2021
Going deeper with Image Transformers
Transformers have been recently adapted for large scale image classifica...
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12/23/2020
Training data-efficient image transformers distillation through attention
Recently, neural networks purely based on attention were shown to addres...
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11/25/2020
Grafit: Learning fine-grained image representations with coarse labels
This paper tackles the problem of learning a finer representation than t...
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02/03/2020
Radioactive data: tracing through training
We want to detect whether a particular image dataset has been used to tr...
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08/29/2019
White-box vs Black-box: Bayes Optimal Strategies for Membership Inference
Membership inference determines, given a sample and trained parameters o...
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07/10/2019
Large Memory Layers with Product Keys
This paper introduces a structured memory which can be easily integrated...
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09/17/2018
Déjà Vu: an empirical evaluation of the memorization properties of ConvNets
Convolutional neural networks memorize part of their training data, whic...
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06/08/2018
A neural network catalyzer for multi-dimensional similarity search
This paper aims at learning a function mapping input vectors to an outpu...
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04/26/2018
Link and code: Fast indexing with graphs and compact regression codes
Similarity search approaches based on graph walks have recently attained...
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09/21/2016
