1 citations · 1 across the 6 of their papers we have counts for
7 papers
Quantifying Epistemic Predictive Uncertainty in Conformal Prediction
Siu Lun Chau, Soroush H. Zargarbashi, Yusuf Sale +1
We study the problem of quantifying epistemic predictive uncertainty (EPU) -- that is, uncertainty faced at prediction time due to the existence of multiple plausible predictive mo…
EvA: Evolutionary Attacks on Graphs
Mohammad Sadegh Akhondzadeh, Soroush H. Zargarbashi, Jimin Cao +1
Even a slight perturbation in the graph structure can cause a significant drop in the accuracy of graph neural networks (GNNs). Most existing attacks leverage gradient information…
One Sample is Enough to Make Conformal Prediction Robust
Soroush H. Zargarbashi, Mohammad Sadegh Akhondzadeh, Aleksandar Bojchevski
For any black-box model, conformal prediction (CP) returns prediction sets guaranteed to include the true label with high adjustable probability. Robust CP (RCP) extends the guaran…
Optimal Conformal Prediction under Epistemic Uncertainty
Alireza Javanmardi, Soroush H. Zargarbashi, Santo M. A. R. Thies +3
Conformal prediction (CP) is a widely used frequentist framework to quantify uncertainty by constructing prediction sets with user-specified marginal coverage guarantees. In practi…
Robust Conformal Prediction with a Single Binary Certificate
Soroush H. Zargarbashi, Aleksandar Bojchevski
Conformal prediction (CP) converts any model's output to prediction sets with a guarantee to cover the true label with (adjustable) high probability. Robust CP extends this guarant…
Conformal Inductive Graph Neural Networks
Soroush H. Zargarbashi, Aleksandar Bojchevski
Conformal prediction (CP) transforms any model's output into prediction sets guaranteed to include (cover) the true label. CP requires exchangeability, a relaxation of the i.i.d. a…