24 citations · 51 across the 5 of their papers we have counts for
Showing 2020Show all
3 papers · 1 filter
cs.LG2020
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging
Han Guo, Nazneen Fatema Rajani, Peter Hase +2
Influence functions approximate the "influences" of training data-points for test predictions and have a wide variety of applications. Despite the popularity, their computational c…
cs.CL2020
Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?
Peter Hase, Shiyue Zhang, Harry Xie +1
Data collection for natural language (NL) understanding tasks has increasingly included human explanations alongside data points, allowing past works to introduce models that both…
cs.CL2020★ 24 cited
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?
Peter Hase, Mohit Bansal
Algorithmic approaches to interpreting machine learning models have proliferated in recent years. We carry out human subject tests that are the first of their kind to isolate the e…