15 citations · 23 across the 5 of their papers we have counts for
5 papers
Efficiently Vectorized MCMC on Modern Accelerators
Hugh Dance, Pierre Glaser, Peter Orbanz +1
With the advent of automatic vectorization tools (e.g., JAX's ), writing multi-chain MCMC algorithms is often now as simple as invoking those tools on single-chain c…
Distinguishing Cause from Effect with Causal Velocity Models
Johnny Xi, Hugh Dance, Peter Orbanz +1
Bivariate structural causal models (SCM) are often used to infer causal direction by examining their goodness-of-fit under restricted model classes. In this paper, we describe a pa…
Interventional Processes for Causal Uncertainty Quantification
Hugh Dance, Peter Orbanz, Arthur Gretton
Reliable uncertainty quantification for causal effects is crucial in high-stakes applications, but remains challenging when the target is an entire function rather than a scalar es…
Preferential Attachment and Vertex Arrival Times
Benjamin Bloem-Reddy, Peter Orbanz
We study preferential attachment mechanisms in random graphs that are parameterized by (i) a constant bias affecting the degree-biased distribution on the vertex set and (ii) the d…
Random Walk Models of Network Formation and Sequential Monte Carlo Methods for Graphs
Benjamin Bloem-Reddy, Peter Orbanz
We introduce a class of generative network models that insert edges by connecting the starting and terminal vertices of a random walk on the network graph. Within the taxonomy of s…