ScAINet'19 2019 Aug. 12, 2019 to Aug. 13, 2019, Arlington, USA

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Title Speakers Summary Topic Types
Keynote: Recent Advances in Adversarial Machine Learning Nicolas Carlini Adversarial machine learning has progressed rapidly over the past few years, with currently over 1,000 ...
TreeHuggr: Discovering Where Tree-based Classifiers are Vulnerable to Adversarial Attack Bobby Filar Tree-based classifiers like gradient-boosted decision trees (GBDTs) and random forests provide state-of-the-art performance in many ...
Verifiably Robust Machine Learning for Security Yizheng Chen Machine learning has shown impressive results in detecting security events such as malware, spam, phishing, ...
Automatically Learning How to Evade Censorship Dave Levin Researchers and censoring regimes have long engaged in a cat-and-mouse game, leading to increasingly sophisticated ...
Keynote Address: PETs, POTs, and Pitfalls: Rethinking the Protection of Users against Machine Learning Carmela Troncoso In a machine-learning dominated world, users' digital interactions are monitored, and scrutinized in order to ...
Panel: Privacy as a Top-level ML System Concern N/a N/A
Dns2Vec: Exploring Internet Domain Names through Deep Learning Amit Arora The concept of vector space embeddings was first applied in the area of Natural Language ...
Panel: Adversarial AI: Perspectives from Academia and Industry Rajarshi Troncoso N/A