6 citations · 15 across the 6 of their papers we have counts for
6 papers
AI Agents Enable Adaptive Computer Worms
Jonas Guan, Tom Blanchard, Hanna Foerster +3
A computer worm is malware that spreads on a network by replicating itself from one machine to another. Traditional worms, like WannaCry, exploited predetermined vulnerabilities, a…
The Erasure Illusion: Stress-Testing the Generalization of LLM Forgetting Evaluation
Hengrui Jia, Taoran Li, Jonas Guan +1
Machine unlearning aims to remove specific data influences from trained models, a capability essential for adhering to copyright laws and ensuring AI safety. Current unlearning met…
Temporal-Difference Learning Using Distributed Error Signals
Jonas Guan, Shon Eduard Verch, Claas Voelcker +3
A computational problem in biological reward-based learning is how credit assignment is performed in the nucleus accumbens (NAc). Much research suggests that NAc dopamine encodes t…
Dataset Inference for Self-Supervised Models
Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem +5
Self-supervised models are increasingly prevalent in machine learning (ML) since they reduce the need for expensively labeled data. Because of their versatility in downstream appli…
On the Difficulty of Defending Self-Supervised Learning against Model Extraction
Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem +2
Self-Supervised Learning (SSL) is an increasingly popular ML paradigm that trains models to transform complex inputs into representations without relying on explicit labels. These…
XDA: Accurate, Robust Disassembly with Transfer Learning
Kexin Pei, Jonas Guan, David Williams-King +2
Accurate and robust disassembly of stripped binaries is challenging. The root of the difficulty is that high-level structures, such as instruction and function boundaries, are abse…