1k citations · 2.2k across the 34 of their papers we have counts for
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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…
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…
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-…
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…
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…