51 citations · 94 across the 8 of their papers we have counts for
5 papers · 1 filter
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples
Sven Gowal, Chongli Qin, Jonathan Uesato +2
Adversarial training and its variants have become de facto standards for learning robust deep neural networks. In this paper, we explore the landscape around adversarial training i…
Non-Stationary Delayed Bandits with Intermediate Observations
Claire Vernade, Andras Gyorgy, Timothy Mann
Online recommender systems often face long delays in receiving feedback, especially when optimizing for some long-term metrics. While mitigating the effects of delays in learning i…
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…
Actively Learning to Attract Followers on Twitter
Nir Levine, Timothy A. Mann, Shie Mannor
Twitter, a popular social network, presents great opportunities for on-line machine learning research. However, previous research has focused almost entirely on learning from passi…
Off-policy evaluation for MDPs with unknown structure
Assaf Hallak, François Schnitzler, Timothy Mann +1
Off-policy learning in dynamic decision problems is essential for providing strong evidence that a new policy is better than the one in use. But how can we prove superiority withou…