Black Box
"Black box" refers to systems whose internal workings are opaque, hindering understanding and analysis. Current research focuses on methods to analyze and mitigate the limitations of black-box models, particularly deep neural networks, across diverse applications like code generation, robot design, and autonomous systems. Key approaches involve developing surrogate models, employing novel optimization techniques, and designing explainable AI (XAI) methods to enhance interpretability and trustworthiness. This research is crucial for ensuring the safety, reliability, and fairness of increasingly prevalent AI systems in various fields.
Papers
Modular addition without black-boxes: Compressing explanations of MLPs that compute numerical integration
Chun Hei Yip, Rajashree Agrawal, Lawrence Chan, Jason Gross
Best-of-N Jailbreaking
John Hughes, Sara Price, Aengus Lynch, Rylan Schaeffer, Fazl Barez, Sanmi Koyejo, Henry Sleight, Erik Jones, Ethan Perez, Mrinank Sharma
FedDTPT: Federated Discrete and Transferable Prompt Tuning for Black-Box Large Language Models
Jiaqi Wu, Simin Chen, Yuzhe Yang, Yijiang Li, Shiyue Hou, Rui Jing, Zehua Wang, Wei Chen, Zijian Tian
Black-Box Forgetting
Yusuke Kuwana, Yuta Goto, Takashi Shibata, Go Irie
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks
Quan Zhou, Changhua Pei, Fei Sun, Jing Han, Zhengwei Gao, Dan Pei, Haiming Zhang, Gaogang Xie, Jianhui Li