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cs.LG2025
Learning from Preferences and Mixed Demonstrations in General Settings
Jason R Brown, Carl Henrik Ek, Robert D Mullins
Reinforcement learning is a general method for learning in sequential settings, but it can often be difficult to specify a good reward function when the task is complex. In these c…
cs.LG2022
Revisiting Embeddings for Graph Neural Networks
S. Purchase, A. Zhao, R. D. Mullins
Current graph representation learning techniques use Graph Neural Networks (GNNs) to extract features from dataset embeddings. In this work, we examine the quality of these embeddi…