Search Results for author:
Found 6 papers, 1 papers with code
Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Approximate Unlearning Completeness
Our analysis, conducted across multiple unlearning benchmarks, reveals that these algorithms inconsistently fulfill their unlearning commitments due to two main issues: 1) unlearning new data can significantly affect the unlearning utility of previously requested data, and 2) approximate algorithms fail to ensure equitable unlearning utility across different groups.
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.
