6 papers
ChronoSSM: Training for Temporally Aware Representations in Autoregressive State Space Models
Adrien Schoen, Nachiketa Ratnakar Patil, Arjun Bhagoji +1
Modern sequence models, from Transformers to State Space Models, have enabled powerful generative modeling across diverse domains, yet they are typically trained to predict what ha…
Jailbreaking for the Average Jane: Choosing Optimal Jailbreaks via Bandit Algorithms for Automatically Enhanced Queries
Prarabdh Shukla, Ritik, Suhas Rao +2
With a profusion of jailbreaks for LLMs now widely known, a growing concern is that non-expert malicious actors ("the average Jane") could elicit actionable responses to malicious…
NetSSM: Multi-Flow and State-Aware Network Trace Generation using State Space Models
Andrew Chu, Xi Jiang, Shinan Liu +4
Access to raw network traffic data is essential for many computer networking tasks, from traffic modeling to performance evaluation. Unfortunately, this data is scarce due to high…
Silencing Empowerment, Allowing Bigotry: Auditing the Moderation of Hate Speech on Twitch
Prarabdh Shukla, Wei Yin Chong, Yash Patel +3
To meet the demands of content moderation, online platforms have resorted to automated systems. Newer forms of real-time engagement(, users commenting on live stream…
Adapting to Evolving Adversaries with Regularized Continual Robust Training
Sihui Dai, Christian Cianfarani, Arjun Bhagoji +2
Robust training methods typically defend against specific attack types, such as Lp attacks with fixed budgets, and rarely account for the fact that defenders may encounter new atta…
MYCROFT: Towards Effective and Efficient External Data Augmentation
Zain Sarwar, Van Tran, Arjun Nitin Bhagoji +3
Machine learning (ML) models often require large amounts of data to perform well. When the available data is limited, model trainers may need to acquire more data from external sou…