3 citations · 5 across the 5 of their papers we have counts for
5 papers
Same Pipeline, Opposite Conclusions: Sample-Surface Effects in Breaking-News Latency
Farhad Bazyari, Xianghang Liu, Sean Moran
Osborne and Dredze (2014) reported that Twitter was the timeliest social-media source of breaking news, trailing only newswire. Twelve years on, the platform landscape has shifted…
FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems
Francesco Fabbri, Xianghang Liu, Jack R. McKenzie +2
Federated Learning (FL) has emerged as a key approach for distributed machine learning, enhancing online personalization while ensuring user data privacy. Instead of sending privat…
Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction
Xianghang Liu, Bartłomiej Twardowski, Tri Kurniawan Wijaya
In Federated Learning (FL) of click-through rate (CTR) prediction, users' data is not shared for privacy protection. The learning is performed by training locally on client devices…
Projecting Markov Random Field Parameters for Fast Mixing
Xianghang Liu, Justin Domke
Markov chain Monte Carlo (MCMC) algorithms are simple and extremely powerful techniques to sample from almost arbitrary distributions. The flaw in practice is that it can take a la…
Projecting Ising Model Parameters for Fast Mixing
Justin Domke, Xianghang Liu
Inference in general Ising models is difficult, due to high treewidth making tree-based algorithms intractable. Moreover, when interactions are strong, Gibbs sampling may take expo…