2 citations · 5 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
Online Learning and Unlearning
Yaxi Hu, Bernhard Schölkopf, Amartya Sanyal
We formalize the problem of online learning-unlearning, where a model is updated sequentially in an online setting while accommodating unlearning requests between updates. After a…
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…
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…
How unfair is private learning ?
Amartya Sanyal, Yaxi Hu, Fanny Yang
As machine learning algorithms are deployed on sensitive data in critical decision making processes, it is becoming increasingly important that they are also private and fair. In t…