Neil Hurley

Bayesian Hierarchical Mixture Clustering using Multilevel Hierarchical Dirichlet Processes

2019 · arXiv (Cornell University)

This paper focuses on the problem of hierarchical non-overlapping clustering of a dataset. In such a clustering, each data item is associated with exactly one leaf node and each internal node is associated with all …

Scalable Deep Q-Learning for Session-Based Slate Recommendation

2023

Reinforcement learning (RL) has demonstrated great potential to improve slate-based recommender systems by optimizing recommendations for long-term user engagement. To handle the combinatorial action space in slate recommendation, recent works decompose the Q-value of a …

FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems

2020

Federated learning (FL) is quickly becoming the de facto standard for the distributed training of deep recommendation models, using on-device user data and reducing server costs. In a typical FL process, a central server tasks …