4 citations · 9 across the 8 of their papers we have counts for
9 papers
Conformal Prediction for Dyadic Regression Under Complex Missingness
Robert Lunde, Minjie Yang, Elizaveta Levina +1
We develop a framework for conformal prediction in dyadic regression problems under complex missingness mechanisms. At the theoretical level, we develop general technical tools for…
On the Validity of Conformal Prediction for Network Data Under Non-Uniform Sampling
Robert Lunde
We study the properties of conformal prediction for network data under various sampling mechanisms that commonly arise in practice but often result in a non-representative sample o…
Conformal Prediction for Network-Assisted Regression
Robert Lunde, Elizaveta Levina, Ji Zhu
An important problem in network analysis is predicting a node attribute using both network covariates, such as graph embedding coordinates or local subgraph counts, and conventiona…
Bootstrapping the error of Oja's algorithm
Robert Lunde, Purnamrita Sarkar, Rachel Ward
We consider the problem of quantifying uncertainty for the estimation error of the leading eigenvector from Oja's algorithm for streaming principal component analysis, where the da…
Trading off Accuracy for Speedup: Multiplier Bootstraps for Subgraph Counts
Qiaohui Lin, Robert Lunde, Purnamrita Sarkar
We propose a new class of multiplier bootstraps for count functionals, ranging from a fast, approximate linear bootstrap tailored to sparse, massive graphs to a quadratic bootstrap…
On the Theoretical Properties of the Network Jackknife
Qiaohui Lin, Robert Lunde, Purnamrita Sarkar
We study the properties of a leave-node-out jackknife procedure for network data. Under the sparse graphon model, we prove an Efron-Stein-type inequality, showing that the network…