8 papers
Anomaly-Informed Confidence Calibration for Vision-Based Safety Prediction
Zhenjiang Mao, Jiawen Wu, Gabriel Wagner +2
Reliable confidence estimates are important for safely deploying vision-based controllers in autonomous racing, where safety predictions must be derived from camera images, yet mod…
Physically Interpretable World Models via Weakly Supervised Representation Learning
Zhenjiang Mao, Mrinall Eashaan Umasudhan, Ivan Ruchkin
Learning predictive models from high-dimensional sensory observations is fundamental for cyber-physical systems, yet the latent representations learned by standard world models lac…
How Safe Will I Be Given What I Saw? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy
Zhenjiang Mao, Mrinall Eashaan Umasudhan, Ivan Ruchkin
Autonomous robots that rely on deep neural network controllers pose critical challenges for safety prediction, especially under partial observability and distribution shift. Tradit…
Confidence over Time: Confidence Calibration with Temporal Logic for Large Language Model Reasoning
Zhenjiang Mao, Anirudhh Venkat, Artem Bisliouk +4
Large Language Models (LLMs) increasingly rely on long-form, multi-step reasoning to solve complex tasks such as mathematical problem solving and scientific question answering. Des…
Recurrent Confidence Chain: Temporal-Aware Uncertainty Quantification in Large Language Models
Zhenjiang Mao, Anirudhh Venkat
As reasoning modules, such as the chain-of-thought mechanism, are applied to large language models, they achieve strong performance on various tasks such as answering common-sense…
Generalizable Image Repair for Robust Visual Control
Carson Sobolewski, Zhenjiang Mao, Kshitij Maruti Vejre +1
Vision-based control relies on accurate perception to achieve robustness. However, image distribution changes caused by sensor noise, adverse weather, and dynamic lighting can degr…