1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2026
LoRA and Privacy: When Random Projections Help (and When They Don't)
Yaxi Hu, Johanna Düngler, Bernhard Schölkopf +1
We introduce the (Wishart) projection mechanism, a randomized map of the form with and study its differential privacy properties. For ve…
cs.LG2024★ 1 cited
Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation
Amartya Sanyal, Yaxi Hu, Yaodong Yu +3
"Accuracy-on-the-line" is a widely observed phenomenon in machine learning, where a model's accuracy on in-distribution (ID) and out-of-distribution (OOD) data is positively correl…
cs.LG2023★ 1 cited
PILLAR: How to make semi-private learning more effective
Francesco Pinto, Yaxi Hu, Fanny Yang +1
In Semi-Supervised Semi-Private (SP) learning, the learner has access to both public unlabelled and private labelled data. We propose a computationally efficient algorithm that, un…