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Found 7 papers, 2 papers with code
On the Fundamental Limits of Formally (Dis)Proving Robustness in Proof-of-Learning
We contribute a formal analysis of why the PoL protocol cannot be formally (dis)proven to be robust against spoofing adversaries.
Selective Classification Via Neural Network Training Dynamics
Selective classification is the task of rejecting inputs a model would predict incorrectly on through a trade-off between input space coverage and model accuracy.
On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning
Machine unlearning, i. e. having a model forget about some of its training data, has become increasingly more important as privacy legislation promotes variants of the right-to-be-forgotten.
Unrolling SGD: Understanding Factors Influencing Machine Unlearning
In this work, we first taxonomize approaches and metrics of approximate unlearning.
SoK: Machine Learning Governance
The application of machine learning (ML) in computer systems introduces not only many benefits but also risks to society.
Proof-of-Learning: Definitions and Practice
In particular, our analyses and experiments show that an adversary seeking to illegitimately manufacture a proof-of-learning needs to perform *at least* as much work than is needed for gradient descent itself.
