2 citations · 2 across the 5 of their papers we have counts for
5 papers
Detection of adversarial intent in Human-AI teams using LLMs
Abed K. Musaffar, Ambuj Singh, Francesco Bullo
Large language models (LLMs) are increasingly deployed in human-AI teams as support agents for complex tasks such as information retrieval, programming, and decision-making assista…
FlowSymm: Physics Aware, Symmetry Preserving Graph Attention for Network Flow Completion
Ege Demirci, Francesco Bullo, Ananthram Swami +1
Recovering missing flows on the edges of a network, while exactly respecting local conservation laws, is a fundamental inverse problem that arises in many systems such as transport…
Learning to Lie: Reinforcement Learning Attacks Damage Human-AI Teams and Teams of LLMs
Abed Kareem Musaffar, Anand Gokhale, Sirui Zeng +4
As artificial intelligence (AI) assistants become more widely adopted in safety-critical domains, it becomes important to develop safeguards against potential failures or adversari…
Learning Neural Contracting Dynamics: Extended Linearization and Global Guarantees
Sean Jaffe, Alexander Davydov, Deniz Lapsekili +2
Global stability and robustness guarantees in learned dynamical systems are essential to ensure well-behavedness of the systems in the face of uncertainty. We present Extended Line…
IDKM: Memory Efficient Neural Network Quantization via Implicit, Differentiable k-Means
Sean Jaffe, Ambuj K. Singh, Francesco Bullo
Compressing large neural networks with minimal performance loss is crucial to enabling their deployment on edge devices. (Cho et al., 2022) proposed a weight quantization method th…