7 citations · 10 across the 3 of their papers we have counts for
3 papers
cs.LG2026
The Finetuner's Fallacy: When to Pretrain with Your Finetuning Data
Christina Baek, Ricardo Pio Monti, David Schwab +31
Real-world model deployments demand strong performance on narrow domains where data is often scarce. Typically, practitioners finetune models to specialize them, but this risks ove…
cs.LG2019★ 7 cited
Randomized Ablation Feature Importance
Luke Merrick
Given a model that predicts a target from a vector of input features , we seek to measure the importance of each feature with respect to th…
cs.LG2019★ 3 cited
The Explanation Game: Explaining Machine Learning Models Using Shapley Values
Luke Merrick, Ankur Taly
A number of techniques have been proposed to explain a machine learning model's prediction by attributing it to the corresponding input features. Popular among these are techniques…