5 citations · 8 across the 2 of their papers we have counts for
4 papers
AI-assisted Coding with Cody: Lessons from Context Retrieval and Evaluation for Code Recommendations
Jan Hartman, Rishabh Mehrotra, Hitesh Sagtani +5
In this work, we discuss a recently popular type of recommender system: an LLM-based coding assistant. Connecting the task of providing code recommendations in multiple formats to…
Local Linear Forests
Rina Friedberg, Julie Tibshirani, Susan Athey +1
Random forests are a powerful method for non-parametric regression, but are limited in their ability to fit smooth signals, and can show poor predictive performance in the presence…
Generalized Random Forests
Susan Athey, Julie Tibshirani, Stefan Wager
We propose generalized random forests, a method for non-parametric statistical estimation based on random forests (Breiman, 2001) that can be used to fit any quantity of interest i…
Robust Logistic Regression using Shift Parameters (Long Version)
Julie Tibshirani, Christopher D. Manning
Annotation errors can significantly hurt classifier performance, yet datasets are only growing noisier with the increased use of Amazon Mechanical Turk and techniques like distant…