6 citations · 6 across the 4 of their papers we have counts for
5 papers
Showing versus Doing: Teaching by Demonstration
Mark K. Ho, Michael L. Littman, James MacGlashan +2
People often learn from others' demonstrations, and inverse reinforcement learning (IRL) techniques have realized this capacity in machines. In contrast, teaching by demonstration…
TEMPO: Transformers for Temporal Disease Progression from Cross-Sectional Data
Hongtao Hao, Joseph L. Austerweil
Event-Based Models (EBMs) infer biomarker progression from cross-sectional data but typically only as ordinal sequences and rely on rigid model assumptions. We propose \textsc{Temp…
Joint Progression Modeling (JPM): A Probabilistic Framework for Mixed-Pathology Progression
Hongtao Hao, Joseph L. Austerweil
Event-based models (EBMs) infer disease progression from cross-sectional data, and standard EBMs assume a single underlying disease per individual. In contrast, mixed pathologies a…
Bayesian Event-Based Model for Disease Subtype and Stage Inference
Hongtao Hao, Joseph L. Austerweil
Chronic diseases often progress differently across patients. Rather than randomly varying, there are typically a small number of subtypes for how a disease progresses across patien…
Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners
Yun-Shiuan Chuang, Xuezhou Zhang, Yuzhe Ma +3
Successful teaching requires an assumption of how the learner learns - how the learner uses experiences from the world to update their internal states. We investigate what expectat…