9 citations · 13 across the 5 of their papers we have counts for
5 papers · 1 filter
Capacity-Aware Planning and Scheduling in Budget-Constrained Multi-Agent MDPs: A Meta-RL Approach
Manav Vora, Ilan Shomorony, Melkior Ornik
We study capacity- and budget-constrained multi-agent MDPs (CB-MA-MDPs), a class that captures many maintenance and scheduling tasks in which each agent can irreversibly fail and a…
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…
MABSplit: Faster Forest Training Using Multi-Armed Bandits
Mo Tiwari, Ryan Kang, Je-Yong Lee +4
Random forests are some of the most widely used machine learning models today, especially in domains that necessitate interpretability. We present an algorithm that accelerates the…
Faster Maximum Inner Product Search in High Dimensions
Mo Tiwari, Ryan Kang, Je-Yong Lee +5
Maximum Inner Product Search (MIPS) is a ubiquitous task in machine learning applications such as recommendation systems. Given a query vector and atom vectors in -dimension…
BanditPAM: Almost Linear Time -Medoids Clustering via Multi-Armed Bandits
Mo Tiwari, Martin Jinye Zhang, James Mayclin +3
Clustering is a ubiquitous task in data science. Compared to the commonly used -means clustering, -medoids clustering requires the cluster centers to be actual data points an…