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Found 6 papers, 1 papers with code
Has Approximate Machine Unlearning been evaluated properly? From Auditing to Side Effects
The growing concerns surrounding data privacy and security have underscored the critical necessity for machine unlearning--aimed at fully removing data lineage from machine learning models.
MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learning
Adapting large language models (LLMs) to new domains/tasks and enabling them to be efficient lifelong learners is a pivotal challenge.
Communication Efficient and Provable Federated Unlearning
We start by giving a rigorous definition of \textit{exact} federated unlearning, which guarantees that the unlearned model is statistically indistinguishable from the one trained without the deleted data.
Differentially Private Non-convex Learning for Multi-layer Neural Networks
This paper focuses on the problem of Differentially Private Stochastic Optimization for (multi-layer) fully connected neural networks with a single output node.
Inductive Graph Unlearning
To extend machine unlearning to graph data, \textit{GraphEraser} has been proposed.
High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantization
In heavy-tailed regime, while the rates of our estimators become essentially slower, these results are either the first ones in an 1-bit quantized and heavy-tailed setting, or already improve on existing comparable results from some respect.
