436 citations · 2.1k across the 92 of their papers we have counts for
9 papers · 1 filter
Hierarchical Bayesian Bandits
Joey Hong, Branislav Kveton, Manzil Zaheer +1
Meta-, multi-task, and federated learning can be all viewed as solving similar tasks, drawn from a distribution that reflects task similarities. We provide a unified view of all th…
When in Doubt, Summon the Titans: Efficient Inference with Large Models
Ankit Singh Rawat, Manzil Zaheer, Aditya Krishna Menon +2
Scaling neural networks to "large" sizes, with billions of parameters, has been shown to yield impressive results on many challenging problems. However, the inference cost incurred…
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu +50
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy prote…
No Regrets for Learning the Prior in Bandits
Soumya Basu, Branislav Kveton, Manzil Zaheer +1
We propose , a Thompson sampling algorithm that adapts sequentially to bandit tasks that it interacts with. The key idea in is to adapt to an unknown tas…
Thompson Sampling with a Mixture Prior
Joey Hong, Branislav Kveton, Manzil Zaheer +2
We study Thompson sampling (TS) in online decision making, where the uncertain environment is sampled from a mixture distribution. This is relevant in multi-task learning, where a…
Exact and Approximate Hierarchical Clustering Using A*
Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath +6
Hierarchical clustering is a critical task in numerous domains. Many approaches are based on heuristics and the properties of the resulting clusterings are studied post hoc. Howeve…