18 citations · 44 across the 6 of their papers we have counts for
7 papers
Debiased Counterfactual Generation via Flow Matching from Observations
Hugh Dance, Johnny Xi, Peter Orbanz +1
Estimating counterfactual distributions under interventions is central to treatment risk assessment and counterfactual generation tasks. Existing approaches model the counterfactua…
Subsampling large graphs and invariance in networks
Peter Orbanz
Specify a randomized algorithm that, given a very large graph or network, extracts a random subgraph. What can we learn about the input graph from a single subsample? We derive law…
Uniform estimation in stochastic block models is slow
Ismaël Castillo, Peter Orbanz
We explicitly quantify the empirically observed phenomenon that estimation under a stochastic block model (SBM) is hard if the model contains classes that are similar. More precise…
Borel Liftings of Graph Limits
Peter Orbanz, Balazs Szegedy
The cut pseudo-metric on the space of graph limits induces an equivalence relation. The quotient space obtained by collapsing each equivalence class to a point is a metric space wi…
Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures
Peter Orbanz, Daniel M. Roy
The natural habitat of most Bayesian methods is data represented by exchangeable sequences of observations, for which de Finetti's theorem provides the theoretical foundation. Diri…
Conjugate Projective Limits
Peter Orbanz
We characterize conjugate nonparametric Bayesian models as projective limits of conjugate, finite-dimensional Bayesian models. In particular, we identify a large class of nonparame…
Projective Limit Random Probabilities on Polish Spaces
Peter Orbanz
A pivotal problem in Bayesian nonparametrics is the construction of prior distributions on the space M(V) of probability measures on a given domain V. In principle, such distributi…