47 citations · 305 across the 34 of their papers we have counts for
6 papers · 1 filter
Graph-based Complexity for Causal Effect by Empirical Plug-in
Rina Dechter, Annie Raichev, Alexander Ihler +1
This paper focuses on the computational complexity of computing empirical plug-in estimates for causal effect queries. Given a causal graph and observational data, any identifiable…
Estimating Causal Effects from Learned Causal Networks
Anna Raichev, Alexander Ihler, Jin Tian +1
The standard approach to answering an identifiable causal-effect query (e.g., ) when given a causal diagram and observational data is to first generate an estimand, or p…
A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation
Max Welling, Andrew E. Gelfand, Alexander T. Ihler
We introduce a new cluster-cumulant expansion (CCE) based on the fixed points of iterative belief propagation (IBP). This expansion is similar in spirit to the loop-series (LS) rec…
Belief Propagation for Structured Decision Making
Qiang Liu, Alexander T. Ihler
Variational inference algorithms such as belief propagation have had tremendous impact on our ability to learn and use graphical models, and give many insights for developing or un…
Join-graph based cost-shifting schemes
Alexander T. Ihler, Natalia Flerova, Rina Dechter +1
We develop several algorithms taking advantage of two common approaches for bounding MPE queries in graphical models: minibucket elimination and message-passing updates for linear…
Accuracy Bounds for Belief Propagation
Alexander T. Ihler
The belief propagation (BP) algorithm is widely applied to perform approximate inference on arbitrary graphical models, in part due to its excellent empirical properties and perfor…