10 papers
CoCoNav: Conformal Control for Safe Robot Navigation in Crowds
Cheng Guo, Mingzhe Ni, Zheng Liang +5
Safe and efficient robot navigation in crowds requires anticipating pedestrian motion despite uncertain and potentially shifting prediction errors. Existing reactive methods can pr…
LHM-Humanoid: Long-Horizon Human Motion Control for Continuous Object Transport in Cluttered Scenes
Haozhuo Zhang, Jingkai Sun, Michele Caprio +5
Physics-based human motion control can make a simulated character walk, sit, and manipulate objects with high physical realism. Almost always, though, this happens in short, isolat…
Adaptive Conformal Prediction for Quantum Machine Learning
Douglas Spencer, Samual Nicholls, Michele Caprio
Quantum machine learning seeks to leverage quantum computers to improve upon classical machine learning algorithms. Currently, robust uncertainty quantification methods remain unde…
Integral Imprecise Probability Metrics
Siu Lun Chau, Michele Caprio, Krikamol Muandet
Quantifying differences between probability distributions is fundamental to statistics and machine learning, primarily for comparing statistical uncertainty. In contrast, epistemic…
A Category-Theoretic Analysis of Conformal Prediction
Michele Caprio
Conformal prediction (CP) produces prediction regions with finite-sample, distribution free coverage guarantees, but its interpretation as a quantitative uncertainty tool is often…
Epistemic Errors of Imperfect Multitask Learners When Distributions Shift
Sabina J. Sloman, Michele Caprio, Samuel Kaski
Uncertainty-aware machine learners, such as Bayesian neural networks, output a quantification of uncertainty instead of a point prediction. We provide uncertainty-aware learners wi…