Develops scalable clustering methods for network-aware A/B testing in two-sided markets that cut spillover while boosting sample size and power, plus a theoretical bias correction.
In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
A factorized generative Markov model is proposed for distributed computing systems to enable tractable simulation, inference, and policy learning, shown in a collaborative AI inference case study.
STRIKE is a proposed unified taxonomy for cybercrimes organized by attack vectors, tactics, societal impact, detection methods, and mitigation approaches.
citing papers explorer
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Scalable Network-Aware Experiment Design for Two-Sided Marketplaces
Develops scalable clustering methods for network-aware A/B testing in two-sided markets that cut spillover while boosting sample size and power, plus a theoretical bias correction.
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Brief Announcement: Generative Markov Model for Distributed Computing Systems
A factorized generative Markov model is proposed for distributed computing systems to enable tractable simulation, inference, and policy learning, shown in a collaborative AI inference case study.
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STRIKE: A Structured Taxonomy of Cybercrime for Risk, Impact, Knowledge, and Evolution
STRIKE is a proposed unified taxonomy for cybercrimes organized by attack vectors, tactics, societal impact, detection methods, and mitigation approaches.