1.1k citations · 1.2k across the 24 of their papers we have counts for
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Towards Understanding How Machines Can Learn Causal Overhypotheses
Eliza Kosoy, David M. Chan, Adrian Liu +7
Recent work in machine learning and cognitive science has suggested that understanding causal information is essential to the development of intelligence. The extensive literature…
Learning Causal Overhypotheses through Exploration in Children and Computational Models
Eliza Kosoy, Adrian Liu, Jasmine Collins +7
Despite recent progress in reinforcement learning (RL), RL algorithms for exploration still remain an active area of research. Existing methods often focus on state-based metrics,…
ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations
Daniel Seita, David Chan, Roshan Rao +3
Learning from demonstrations is a popular tool for accelerating and reducing the exploration requirements of reinforcement learning. When providing expert demonstrations to human s…
t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern Data
David M. Chan, Roshan Rao, Forrest Huang +1
Modern datasets and models are notoriously difficult to explore and analyze due to their inherent high dimensionality and massive numbers of samples. Existing visualization methods…