22 citations · 62 across the 28 of their papers we have counts for
8 papers · 1 filter
Safe Reinforcement Learning with Probabilistic Control Barrier Functions for Ramp Merging
Soumith Udatha, Yiwei Lyu, John Dolan
Prior work has looked at applying reinforcement learning and imitation learning approaches to autonomous driving scenarios, but either the safety or the efficiency of the algorithm…
Nano: Nested Human-in-the-Loop Reward Learning for Few-shot Language Model Control
Xiang Fan, Yiwei Lyu, Paul Pu Liang +2
Pretrained language models have demonstrated extraordinary capabilities in language generation. However, real-world tasks often require controlling the distribution of generated te…
MultiViz: Towards Visualizing and Understanding Multimodal Models
Paul Pu Liang, Yiwei Lyu, Gunjan Chhablani +5
The promise of multimodal models for real-world applications has inspired research in visualizing and understanding their internal mechanics with the end goal of empowering stakeho…
Responsibility-associated Multi-agent Collision Avoidance with Social Preferences
Yiwei Lyu, Wenhao Luo, John M. Dolan
This paper introduces a novel social preference-aware decentralized safe control framework to address the responsibility allocation problem in multi-agent collision avoidance. Cons…
Provable Probabilistic Safety and Feasibility-Assured Control for Autonomous Vehicles using Exponential Control Barrier Functions
Spencer Van Koevering, Yiwei Lyu, Wenhao Luo +1
With the increasing need for safe control in the domain of autonomous driving, model-based safety-critical control approaches are widely used, especially Control Barrier Function (…
DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations
Yiwei Lyu, Paul Pu Liang, Zihao Deng +2
The ability for a human to understand an Artificial Intelligence (AI) model's decision-making process is critical in enabling stakeholders to visualize model behavior, perform mode…
High-Modality Multimodal Transformer: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation Learning
Paul Pu Liang, Yiwei Lyu, Xiang Fan +6
Many real-world problems are inherently multimodal, from spoken language, gestures, and paralinguistics humans use to communicate, to force, proprioception, and visual sensors on r…
Adaptive Safe Merging Control for Heterogeneous Autonomous Vehicles using Parametric Control Barrier Functions
Yiwei Lyu, Wenhao Luo, John M. Dolan
With the increasing emphasis on the safe autonomy for robots, model-based safe control approaches such as Control Barrier Functions have been extensively studied to ensure guarante…