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Found 4 papers, 1 papers with code
HTP: Exploiting Holistic Temporal Patterns for Sequential Recommendation
In this paper, we systematically investigate various temporal information for sequential recommendation and identify three types of advantageous temporal patterns beyond order, including absolute time information, relative item time intervals and relative recommendation time intervals.
Support Vector Machines under Adversarial Label Contamination
Machine learning algorithms are increasingly being applied in security-related tasks such as spam and malware detection, although their security properties against deliberate attacks have not yet been widely understood.
Wasserstein Adversarial Imitation Learning
Imitation Learning describes the problem of recovering an expert policy from demonstrations.
Is feature selection secure against training data poisoning?
Learning in adversarial settings is becoming an important task for application domains where attackers may inject malicious data into the training set to subvert normal operation of data-driven technologies.
