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20212026
most citedSmall Effect Sizes in Malware Detection? Make Harder Train/Test Splits!

4 citations · 13 across the 18 of their papers we have counts for

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cs.LG2026

Scaling Patterns in Adversarial Alignment: Evidence from Multi-LLM Jailbreak Experiments

Samuel Nathanson, Rebecca Williams, Cynthia Matuszek

Large language models (LLMs) increasingly operate in multi-agent and safety-critical settings, raising open questions about how their vulnerabilities scale when models interact adv…

cs.LG2025

Topic Modeling and Link-Prediction for Material Property Discovery

Ryan C. Barron, Maksim E. Eren, Valentin Stanev +2

Link prediction infers missing or future relations between graph nodes, based on connection patterns. Scientific literature networks and knowledge graphs are typically large, spars…

cs.LG2025

Matrix Factorization for Inferring Associations and Missing Links

Ryan Barron, Maksim E. Eren, Duc P. Truong +4

Missing link prediction is a method for network analysis, with applications in recommender systems, biology, social sciences, cybersecurity, information retrieval, and Artificial I…

cs.LG2023★ 4 cited

Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits!

Tirth Patel, Fred Lu, Edward Raff +3

Industry practitioners care about small improvements in malware detection accuracy because their models are deployed to hundreds of millions of machines, meaning a 0.1\% change can…

cs.LG2023

DDxT: Deep Generative Transformer Models for Differential Diagnosis

Mohammad Mahmudul Alam, Edward Raff, Tim Oates +1

Differential Diagnosis (DDx) is the process of identifying the most likely medical condition among the possible pathologies through the process of elimination based on evidence. An…

cs.LG2023★ 2 cited

Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognition

Luke E. Richards, Edward Raff, Cynthia Matuszek

Over the past decade, the machine learning security community has developed a myriad of defenses for evasion attacks. An understudied question in that community is: for whom do the…