13 citations · 34 across the 15 of their papers we have counts for
5 papers · 1 filter
Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles
Edward Grefenstette, Robert Stanforth, Brendan O'Donoghue +3
While deep learning has led to remarkable results on a number of challenging problems, researchers have discovered a vulnerability of neural networks in adversarial settings, where…
Globally Convergent Type-I Anderson Acceleration for Non-Smooth Fixed-Point Iterations
Junzi Zhang, Brendan O'Donoghue, Stephen Boyd
We consider the application of the type-I Anderson acceleration to solving general non-smooth fixed-point problems. By interleaving with safe-guarding steps, and employing a Powell…
Variational Bayesian Reinforcement Learning with Regret Bounds
Brendan O'Donoghue
In reinforcement learning the Q-values summarize the expected future rewards that the agent will attain. However, they cannot capture the epistemic uncertainty about those rewards.…
Training verified learners with learned verifiers
Krishnamurthy Dvijotham, Sven Gowal, Robert Stanforth +4
This paper proposes a new algorithmic framework, predictor-verifier training, to train neural networks that are verifiable, i.e., networks that provably satisfy some desired input-…
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
Jonathan Uesato, Brendan O'Donoghue, Aaron van den Oord +1
This paper investigates recently proposed approaches for defending against adversarial examples and evaluating adversarial robustness. We motivate 'adversarial risk' as an objectiv…