3 citations · 7 across the 13 of their papers we have counts for
6 papers · 1 filter
Viscosity CBFs: Bridging the Control Barrier Function and Hamilton-Jacobi Reachability Frameworks in Safe Control Theory
Dylan Hirsch, Jaime Fernández Fisac, Sylvia Herbert
Control barrier functions (CBFs) and Hamilton-Jacobi reachability (HJR) are central frameworks in safe control. Traditionally, these frameworks have been viewed as distinct, with t…
Provably Optimal Reinforcement Learning under Safety Filtering
Donggeon David Oh, Duy P. Nguyen, Haimin Hu +2
Recent advances in reinforcement learning (RL) enable its use on increasingly complex tasks, but the lack of formal safety guarantees still limits its application in safety-critica…
From Refusal to Recovery: A Control-Theoretic Approach to Generative AI Guardrails
Ravi Pandya, Madison Bland, Duy P. Nguyen +3
Generative AI systems are increasingly assisting and acting on behalf of end users in practical settings, from digital shopping assistants to next-generation autonomous cars. In th…
Machine Bullshit: Characterizing the Emergent Disregard for Truth in Large Language Models
Kaiqu Liang, Haimin Hu, Xuandong Zhao +3
Bullshit, as conceptualized by philosopher Harry Frankfurt, refers to statements made without regard to their truth value. While previous work has explored large language model (LL…
Safety with Agency: Human-Centered Safety Filter with Application to AI-Assisted Motorsports
Donggeon David Oh, Justin Lidard, Haimin Hu +8
We propose a human-centered safety filter (HCSF) for shared autonomy that significantly enhances system safety without compromising human agency. Our HCSF is built on a neural safe…
RLHS: Mitigating Misalignment in RLHF with Hindsight Simulation
Kaiqu Liang, Haimin Hu, Ryan Liu +2
While Reinforcement Learning from Human Feedback (RLHF) has shown promise in aligning generative AI, we present empirical evidence that it can also cause severe, systematic misalig…