Neural Information Processing Systems http://nips.cc/

Compared to robust submodular maximization for set function, new challenges arise when sequence functions are concerned.

We extend our results to the general case of maximizing a monotone submodular function subject to the intersection of a p-set system and multiple knapsack constraints. Finally, we evaluate the performance of our algorithms on multiple real-lifeapplications, includingmovierecommendation, locationsummarization, Twittertextsummarization,andvideosummarization.

We consider the problem of maximizing submodular functions in single-pass streaming and secretaries-with-shortlists models, both with random arrival order. For cardinality constrained monotone functions, Agrawal, Shadravan, and Stein [ASS19]gaveasingle-pass(1 1/e ε)-approximation algorithm using only linear memory,buttheir exponential dependence onεmakesitimpractical evenforε = 0.1.

We argue that Binarized Neural Networks (BNNs) provide comparable robustness and allow exact and significantly more efficient verification. We present a new system, EEV,forefficient and exact verification ofBNNs.