33 citations · 39 across the 5 of their papers we have counts for
6 papers
A Law of Robustness for Weight-bounded Neural Networks
Hisham Husain, Borja Balle
Robustness of deep neural networks against adversarial perturbations is a pressing concern motivated by recent findings showing the pervasive nature of such vulnerabilities. One me…
Regularized Policies are Reward Robust
Hisham Husain, Kamil Ciosek, Ryota Tomioka
Entropic regularization of policies in Reinforcement Learning (RL) is a commonly used heuristic to ensure that the learned policy explores the state-space sufficiently before overf…
Optimal Continual Learning has Perfect Memory and is NP-hard
Jeremias Knoblauch, Hisham Husain, Tom Diethe
Continual Learning (CL) algorithms incrementally learn a predictor or representation across multiple sequentially observed tasks. Designing CL algorithms that perform reliably and…
Distributional Robustness with IPMs and links to Regularization and GANs
Hisham Husain
Robustness to adversarial attacks is an important concern due to the fragility of deep neural networks to small perturbations and has received an abundance of attention in recent y…
Adversarial Networks and Autoencoders: The Primal-Dual Relationship and Generalization Bounds
Hisham Husain, Richard Nock, Robert C. Williamson
Since the introduction of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAE), the literature on generative modelling has witnessed an overwhelming resurgence…
Integral Privacy for Sampling
Hisham Husain, Zac Cranko, Richard Nock
Differential privacy is a leading protection setting, focused by design on individual privacy. Many applications, in medical / pharmaceutical domains or social networks, rather pos…