2.9k citations · 3.3k across the 26 of their papers we have counts for
27 papers
Private Matrix Factorization with Public Item Features
Mihaela Curmei, Walid Krichene, Li Zhang +1
We consider the problem of training private recommendation models with access to public item features. Training with Differential Privacy (DP) offers strong privacy guarantees, at…
Using Foundation Models to Detect Policy Violations with Minimal Supervision
Sid Mittal, Vineet Gupta, Frederick Liu +1
Foundation models, i.e. large neural networks pre-trained on large text corpora, have revolutionized NLP. They can be instructed directly (e.g. (arXiv:2005.14165)) - this is called…
Multi-Task Differential Privacy Under Distribution Skew
Walid Krichene, Prateek Jain, Shuang Song +3
We study the problem of multi-task learning under user-level differential privacy, in which users contribute data to tasks, each involving a subset of users. One important…
Attributing AUC-ROC to Analyze Binary Classifier Performance
Arya Tafvizi, Besim Avci, Mukund Sundararajan
Area Under the Receiver Operating Characteristic Curve (AUC-ROC) is a popular evaluation metric for binary classifiers. In this paper, we discuss techniques to segment the AUC-ROC…
Reciprocity in Machine Learning
Mukund Sundararajan, Walid Krichene
Machine learning is pervasive. It powers recommender systems such as Spotify, Instagram and YouTube, and health-care systems via models that predict sleep patterns, or the risk of…
First is Better Than Last for Language Data Influence
Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan +2
The ability to identify influential training examples enables us to debug training data and explain model behavior. Existing techniques to do so are based on the flow of training d…