26 citations · 93 across the 13 of their papers we have counts for
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stat.ML2018★ 13 cited
Data-dependent compression of random features for large-scale kernel approximation
Raj Agrawal, Trevor Campbell, Jonathan H. Huggins +1
Kernel methods offer the flexibility to learn complex relationships in modern, large data sets while enjoying strong theoretical guarantees on quality. Unfortunately, these methods…
stat.CO2018★ 7 cited
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models
Raj Agrawal, Tamara Broderick, Caroline Uhler
Learning a Bayesian network (BN) from data can be useful for decision-making or discovering causal relationships. However, traditional methods often fail in modern applications, wh…