1 citations · 1 across the 7 of their papers we have counts for
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Open-Weight LLM Fine-Tuning Defenses are Susceptible to Simple Attacks
Kevin Kuo, Chhavi Yadav, Virginia Smith
Recent defenses for safeguarding open-weight large language models (LLMs) are intended to prevent adversarial usage. Underlying these defenses is an assumption that new harmful beh…
Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice
Kevin Kuo, Chhavi Yadav, Virginia Smith
Cross-silo federated learning (FL) is a promising approach to enable cross-organization collaboration in machine learning model development without directly sharing private data. D…
Exact Unlearning of Finetuning Data via Model Merging at Scale
Kevin Kuo, Amrith Setlur, Kartik Srinivas +2
Approximate unlearning has gained popularity as an approach to efficiently update an LLM so that it behaves (roughly) as if it was not trained on a subset of data to begin with. Ho…
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA
Marlon Tobaben, Mohamed Ali Souibgui, Rubèn Tito +24
The Privacy Preserving Federated Learning Document VQA (PFL-DocVQA) competition challenged the community to develop provably private and communication-efficient solutions in a fede…
Federated LoRA with Sparse Communication
Kevin Kuo, Arian Raje, Kousik Rajesh +1
Low-rank adaptation (LoRA) is a natural method for finetuning in communication-constrained machine learning settings such as cross-device federated learning. Prior work that has st…