5 citations · 11 across the 6 of their papers we have counts for
4 papers · 1 filter
BanditPAM++: Faster -medoids Clustering
Mo Tiwari, Ryan Kang, Donghyun Lee +4
Clustering is a fundamental task in data science with wide-ranging applications. In -medoids clustering, cluster centers must be actual datapoints and arbitrary distance metrics…
In-Context Learning for Few-Shot Molecular Property Prediction
Christopher Fifty, Jure Leskovec, Sebastian Thrun
In-context learning has become an important approach for few-shot learning in Large Language Models because of its ability to rapidly adapt to new tasks without fine-tuning model p…
Context-Aware Meta-Learning
Christopher Fifty, Dennis Duan, Ronald G. Junkins +4
Large Language Models like ChatGPT demonstrate a remarkable capacity to learn new concepts during inference without any fine-tuning. However, visual models trained to detect new ob…
MAPTree: Beating "Optimal" Decision Trees with Bayesian Decision Trees
Colin Sullivan, Mo Tiwari, Sebastian Thrun
Decision trees remain one of the most popular machine learning models today, largely due to their out-of-the-box performance and interpretability. In this work, we present a Bayesi…