6 citations · 8 across the 3 of their papers we have counts for
3 papers
cs.DS2022★ 1 cited
Quasi-stable Coloring for Graph Compression: Approximating Max-Flow, Linear Programs, and Centrality
Moe Kayali, Dan Suciu
We propose quasi-stable coloring, an approximate version of stable coloring. Stable coloring, also called color refinement, is a well-studied technique in graph theory for classify…
cs.LG2022★ 1 cited
Mining Robust Default Configurations for Resource-constrained AutoML
Moe Kayali, Chi Wang
Automatic machine learning (AutoML) is a key enabler of the mass deployment of the next generation of machine learning systems. A key desideratum for future ML systems is the autom…
cs.DB2020★ 6 cited
Causal Relational Learning
Babak Salimi, Harsh Parikh, Moe Kayali +3
Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making. The gold standard in ca…