280 citations · 1.2k across the 24 of their papers we have counts for
7 papers · 1 filter
On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth +6
Recent work has shown that it is possible to train deep neural networks that are provably robust to norm-bounded adversarial perturbations. Most of these methods are based on minim…
Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem
Hugo Penedones, Damien Vincent, Hartmut Maennel +3
Temporal-Difference learning (TD) [Sutton, 1988] with function approximation can converge to solutions that are worse than those obtained by Monte-Carlo regression, even in the sim…
Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems
Timothy A. Mann, Sven Gowal, András György +4
Predicting delayed outcomes is an important problem in recommender systems (e.g., if customers will finish reading an ebook). We formalize the problem as an adversarial, delayed on…
A Dual Approach to Scalable Verification of Deep Networks
Krishnamurthy, Dvijotham, Robert Stanforth +3
This paper addresses the problem of formally verifying desirable properties of neural networks, i.e., obtaining provable guarantees that neural networks satisfy specifications rela…
Soft-Robust Actor-Critic Policy-Gradient
Esther Derman, Daniel J. Mankowitz, Timothy A. Mann +1
Robust Reinforcement Learning aims to derive optimal behavior that accounts for model uncertainty in dynamical systems. However, previous studies have shown that by considering the…
Beyond Greedy Ranking: Slate Optimization via List-CVAE
Ray Jiang, Sven Gowal, Timothy A. Mann +1
The conventional solution to the recommendation problem greedily ranks individual document candidates by prediction scores. However, this method fails to optimize the slate as a wh…