17 citations · 38 across the 25 of their papers we have counts for
4 papers · 1 filter
Uncertainty-Aware Selection of Online Algorithms with Simulator Ensembles
Yongyi Guo, Zifan Xu, Ziping Xu +1
The performance of online reinforcement learning depends critically on design choices, especially those that affect exploration. These choices are often selected by fitting a simul…
Provably Minimum-Length Conformal Prediction Sets for Ordinal Classification
Zijian Zhang, Xinyu Chen, Yuanjie Shi +3
Ordinal classification has been widely applied in many high-stakes applications, e.g., medical imaging and diagnosis, where reliable uncertainty quantification (UQ) is essential fo…
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon +44
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks enc…
Causal Dynamics Learning for Task-Independent State Abstraction
Zizhao Wang, Xuesu Xiao, Zifan Xu +2
Learning dynamics models accurately is an important goal for Model-Based Reinforcement Learning (MBRL), but most MBRL methods learn a dense dynamics model which is vulnerable to sp…