2 citations · 5 across the 5 of their papers we have counts for
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stat.ML2022★ 1 cited
Distribution Free Prediction Sets for Node Classification
Jase Clarkson
Graph Neural Networks (GNNs) are able to achieve high classification accuracy on many important real world datasets, but provide no rigorous notion of predictive uncertainty. Quant…
cs.LG2022★ 2 cited
Time Series Prediction under Distribution Shift using Differentiable Forgetting
Stefanos Bennett, Jase Clarkson
Time series prediction is often complicated by distribution shift which demands adaptive models to accommodate time-varying distributions. We frame time series prediction under dis…
stat.ML2022★ 1 cited
DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series
Jase Clarkson, Mihai Cucuringu, Andrew Elliott +1
Generative models for network time series (also known as dynamic graphs) have tremendous potential in fields such as epidemiology, biology and economics, where complex graph-based…