1.4k citations · 3.1k across the 30 of their papers we have counts for
3 papers · 1 filter
Augmenting Interpretable Models with LLMs during Training
Chandan Singh, Armin Askari, Rich Caruana +1
Recent large language models (LLMs) have demonstrated remarkable prediction performance for a growing array of tasks. However, their proliferation into high-stakes domains (e.g. me…
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
Zijie J. Wang, Alex Kale, Harsha Nori +6
Machine learning (ML) interpretability techniques can reveal undesirable patterns in data that models exploit to make predictions--potentially causing harms once deployed. However,…
Differentially Private Estimation of Heterogeneous Causal Effects
Fengshi Niu, Harsha Nori, Brian Quistorff +3
Estimating heterogeneous treatment effects in domains such as healthcare or social science often involves sensitive data where protecting privacy is important. We introduce a gener…