8 citations · 8 across the 2 of their papers we have counts for
2 papers
cs.CR2024
Differentially Private Training of Mixture of Experts Models
Pierre Tholoniat, Huseyin A. Inan, Janardhan Kulkarni +1
This position paper investigates the integration of Differential Privacy (DP) in the training of Mixture of Experts (MoE) models within the field of natural language processing. As…
cs.LG2022★ 8 cited
When Does Differentially Private Learning Not Suffer in High Dimensions?
Xuechen Li, Daogao Liu, Tatsunori Hashimoto +4
Large pretrained models can be privately fine-tuned to achieve performance approaching that of non-private models. A common theme in these results is the surprising observation tha…