280 citations · 2k across the 81 of their papers we have counts for
5 papers · 2 filters
Adversarial Attacks are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners
Elre T. Oldewage, John Bronskill, Richard E. Turner
This paper examines the robustness of deployed few-shot meta-learning systems when they are fed an imperceptibly perturbed few-shot dataset. We attack amortized meta-learners, whic…
Differentially private partitioned variational inference
Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop +2
Learning a privacy-preserving model from sensitive data which are distributed across multiple devices is an increasingly important problem. The problem is often formulated in the f…
The Neural Process Family: Survey, Applications and Perspectives
Saurav Jha, Dong Gong, Xuesong Wang +2
The standard approaches to neural network implementation yield powerful function approximation capabilities but are limited in their abilities to learn meta representations and rea…
Challenges and Pitfalls of Bayesian Unlearning
Ambrish Rawat, James Requeima, Wessel Bruinsma +1
Machine unlearning refers to the task of removing a subset of training data, thereby removing its contributions to a trained model. Approximate unlearning are one class of methods…
Multi-disciplinary fairness considerations in machine learning for clinical trials
Isabel Chien, Nina Deliu, Richard E. Turner +3
While interest in the application of machine learning to improve healthcare has grown tremendously in recent years, a number of barriers prevent deployment in medical practice. A n…