1.4k citations · 5k across the 110 of their papers we have counts for
13 papers · 1 filter
Who's responsible? Jointly quantifying the contribution of the learning algorithm and training data
Gal Yona, Amirata Ghorbani, James Zou
A learning algorithm trained on a dataset is revealed to have poor performance on some subpopulation at test time. Where should the responsibility for this lay? It can be a…
LitGen: Genetic Literature Recommendation Guided by Human Explanations
Allen Nie, Arturo L. Pineda, Matt W. Wright Hannah Wand +5
As genetic sequencing costs decrease, the lack of clinical interpretation of variants has become the bottleneck in using genetics data. A major rate limiting step in clinical inter…
Learning transport cost from subset correspondence
Ruishan Liu, Akshay Balsubramani, James Zou
Learning to align multiple datasets is an important problem with many applications, and it is especially useful when we need to integrate multiple experiments or correct for confou…
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems
Antonio Ginart, Maxim Naumov, Dheevatsa Mudigere +2
Embedding representations power machine intelligence in many applications, including recommendation systems, but they are space intensive -- potentially occupying hundreds of gigab…
Making AI Forget You: Data Deletion in Machine Learning
Antonio Ginart, Melody Y. Guan, Gregory Valiant +1
Intense recent discussions have focused on how to provide individuals with control over when their data can and cannot be used --- the EU's Right To Be Forgotten regulation is an e…
Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild
Abubakar Abid, Ali Abdalla, Ali Abid +3
Accessibility is a major challenge of machine learning (ML). Typical ML models are built by specialists and require specialized hardware/software as well as ML experience to valida…
Discovering Conditionally Salient Features with Statistical Guarantees
Jaime Roquero Gimenez, James Zou
The goal of feature selection is to identify important features that are relevant to explain an outcome variable. Most of the work in this domain has focused on identifying globall…
A Knowledge Graph-based Approach for Exploring the U.S. Opioid Epidemic
Maulik R. Kamdar, Tymor Hamamsy, Shea Shelton +4
The United States is in the midst of an opioid epidemic with recent estimates indicating that more than 130 people die every day due to drug overdose. The over-prescription and add…
Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings
Dorottya Demszky, Nikhil Garg, Rob Voigt +4
We provide an NLP framework to uncover four linguistic dimensions of political polarization in social media: topic choice, framing, affect and illocutionary force. We quantify thes…
Data Shapley: Equitable Valuation of Data for Machine Learning
Amirata Ghorbani, James Zou
As data becomes the fuel driving technological and economic growth, a fundamental challenge is how to quantify the value of data in algorithmic predictions and decisions. For examp…
Contrastive Variational Autoencoder Enhances Salient Features
Abubakar Abid, James Zou
Variational autoencoders are powerful algorithms for identifying dominant latent structure in a single dataset. In many applications, however, we are interested in modeling latent…
Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits
Martin J. Zhang, James Zou, David Tse
Monte Carlo (MC) permutation test is considered the gold standard for statistical hypothesis testing, especially when standard parametric assumptions are not clear or likely to fai…
Concrete Autoencoders for Differentiable Feature Selection and Reconstruction
Abubakar Abid, Muhammad Fatih Balin, James Zou
We introduce the concrete autoencoder, an end-to-end differentiable method for global feature selection, which efficiently identifies a subset of the most informative features and…