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05/24/2023
Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models
Large language models (LLMs) are excellent in-context learners. However,...
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03/29/2023
Have it your way: Individualized Privacy Assignment for DP-SGD
When training a machine learning model with differential privacy, one se...
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01/09/2023
Is Federated Learning a Practical PET Yet?
Federated learning (FL) is a framework for users to jointly train a mach...
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11/23/2022
Private Multi-Winner Voting for Machine Learning
Private multi-winner voting is the task of revealing k-hot binary vector...
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09/16/2022
Dataset Inference for Self-Supervised Models
Self-supervised models are increasingly prevalent in machine learning (M...
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07/25/2022
p-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations
The lack of well-calibrated confidence estimates makes neural networks i...
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05/26/2022
Selective Classification Via Neural Network Training Dynamics
Selective classification is the task of rejecting inputs a model would p...
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05/16/2022
On the Difficulty of Defending Self-Supervised Learning against Model Extraction
Self-Supervised Learning (SSL) is an increasingly popular ML paradigm th...
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01/23/2022
Increasing the Cost of Model Extraction with Calibrated Proof of Work
In model extraction attacks, adversaries can steal a machine learning mo...
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12/06/2021
When the Curious Abandon Honesty: Federated Learning Is Not Private
In federated learning (FL), data does not leave personal devices when th...
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08/03/2021
On the Exploitability of Audio Machine Learning Pipelines to Surreptitious Adversarial Examples
Machine learning (ML) models are known to be vulnerable to adversarial e...
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02/09/2021
CaPC Learning: Confidential and Private Collaborative Learning
Machine learning benefits from large training datasets, which may not al...
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04/13/2020
Pretrained Transformers Improve Out-of-Distribution Robustness
Although pretrained Transformers such as BERT achieve high accuracy on i...
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03/18/2020
Machine Learning enabled Spectrum Sharing in Dense LTE-U/Wi-Fi Coexistence Scenarios
The application of Machine Learning (ML) techniques to complex engineeri...
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02/08/2020
An Empirical Evaluation of Perturbation-based Defenses
Recent work has extensively shown that randomized perturbations of a neu...
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11/21/2019
Machine Learning based detection of multiple Wi-Fi BSSs for LTE-U CSAT
According to the LTE-U Forum specification, a LTE-U base-station (BS) re...
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11/21/2019
Band-limited Training and Inference for Convolutional Neural Networks
The convolutional layers are core building blocks of neural network arch...
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12/18/2018
DeepLens: Towards a Visual Data Management System
Advances in deep learning have greatly widened the scope of automatic co...
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07/03/2017