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07/01/2023
Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD
Differentially private stochastic gradient descent (DP-SGD) is the canon...
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08/06/2022
On the Fundamental Limits of Formally (Dis)Proving Robustness in Proof-of-Learning
Proof-of-learning (PoL) proposes a model owner use machine learning trai...
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10/22/2021
On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning
Machine unlearning, i.e. having a model forget about some of its trainin...
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09/20/2021
SoK: Machine Learning Governance
The application of machine learning (ML) in computer systems introduces ...
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03/09/2021
Proof-of-Learning: Definitions and Practice
Training machine learning (ML) models typically involves expensive itera...
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02/27/2020
Entangled Watermarks as a Defense against Model Extraction
Machine learning involves expensive data collection and training procedu...
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12/09/2019
