355 citations · 569 across the 25 of their papers we have counts for
6 papers · 1 filter
Verify Less, Evolve More: Training Idea-Level Critics for Verification-Efficient ML Evolving Agents
Jiamu Bai, Lizhu Zhang, Xin Yu +9
As large language models become more powerful, self-evolving agents are able to tackle challenging tasks including AI for machine learning (AI4ML). In AI4ML, while empirical verifi…
Spectrum-Aware Bounds on Invertibility for Privacy-Enhancing Instance Encoding
Seokjin Hwang, Yuting Li, Kiwan Maeng
Instance encoding is a popular empirical technique for privacy enhancement when sharing data to an untrusted server. It transforms sensitive data through an encoding process before…
Trusted Hardware Acceleration for Function Secret Sharing
Pengzhi Huang, Kiwan Maeng, G. Edward Suh
Function secret sharing (FSS) is a core building block for privacy-preserving systems such as secure inference and private information retrieval (PIR), but incurs significant overh…
Characterizing How Complex Agentic AI Systems Handle General Tasks: A Trace-Based Simulation Study
Donghwan Kim, Prakhar Singh, Younghoon Min +3
Agentic AI completes tasks through iterative planning, tool use, and reasoning based on observed outcomes. Despite its popularity, its system-level behavior remains poorly understo…
Beyond Latency: A System-Level Characterization of MPC and FHE for PPML
Pengzhi Huang, Kiwan Maeng, G. Edward Suh
Privacy protection has become an increasing concern in modern machine learning applications. Privacy-preserving machine learning (PPML) has attracted growing research attention, wi…
Composition for Pufferfish Privacy
Jiamu Bai, Guanlin He, Xin Gu +2
When creating public data products out of confidential datasets, inferential/posterior-based privacy definitions, such as Pufferfish, provide compelling privacy semantics for data…