1.4k citations · 5k across the 110 of their papers we have counts for
21 papers · 1 filter
A Spectral Method for Assessing and Combining Multiple Data Visualizations
Rong Ma, Eric D. Sun, James Zou
Dimension reduction and data visualization aim to project a high-dimensional dataset to a low-dimensional space while capturing the intrinsic structures in the data. It is an indis…
Estimating and Explaining Model Performance When Both Covariates and Labels Shift
Lingjiao Chen, Matei Zaharia, James Zou
Deployed machine learning (ML) models often encounter new user data that differs from their training data. Therefore, estimating how well a given model might perform on the new dat…
A Unified f-divergence Framework Generalizing VAE and GAN
Jaime Roquero Gimenez, James Zou
Developing deep generative models that flexibly incorporate diverse measures of probability distance is an important area of research. Here we develop an unified mathematical frame…
Beyond Importance Scores: Interpreting Tabular ML by Visualizing Feature Semantics
Amirata Ghorbani, Dina Berenbaum, Maor Ivgi +2
Interpretability is becoming an active research topic as machine learning (ML) models are more widely used to make critical decisions. Tabular data is one of the most commonly used…
Did the Model Change? Efficiently Assessing Machine Learning API Shifts
Lingjiao Chen, Tracy Cai, Matei Zaharia +1
Machine learning (ML) prediction APIs are increasingly widely used. An ML API can change over time due to model updates or retraining. This presents a key challenge in the usage of…
Data Shapley Valuation for Efficient Batch Active Learning
Amirata Ghorbani, James Zou, Andre Esteva
Annotating the right set of data amongst all available data points is a key challenge in many machine learning applications. Batch active learning is a popular approach to address…
Efficient computation and analysis of distributional Shapley values
Yongchan Kwon, Manuel A. Rivas, James Zou
Distributional data Shapley value (DShapley) has recently been proposed as a principled framework to quantify the contribution of individual datum in machine learning. DShapley dev…
Neuron Shapley: Discovering the Responsible Neurons
Amirata Ghorbani, James Zou
We develop Neuron Shapley as a new framework to quantify the contribution of individual neurons to the prediction and performance of a deep network. By accounting for interactions…
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…
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 Multivariate Singular Spectrum Analysis
Abdi-Hakin Dirie, Abubakar Abid, James Zou
We introduce Contrastive Multivariate Singular Spectrum Analysis, a novel unsupervised method for dimensionality reduction and signal decomposition of time series data. By utilizin…
Improving the Stability of the Knockoff Procedure: Multiple Simultaneous Knockoffs and Entropy Maximization
Jaime Roquero Gimenez, James Zou
The Model-X knockoff procedure has recently emerged as a powerful approach for feature selection with statistical guarantees. The advantage of knockoff is that if we have a good mo…
Knockoffs for the mass: new feature importance statistics with false discovery guarantees
Jaime Roquero Gimenez, Amirata Ghorbani, James Zou
An important problem in machine learning and statistics is to identify features that causally affect the outcome. This is often impossible to do from purely observational data, and…
Stochastic EM for Shuffled Linear Regression
Abubakar Abid, James Zou
We consider the problem of inference in a linear regression model in which the relative ordering of the input features and output labels is not known. Such datasets naturally arise…
Interpretation of Neural Networks is Fragile
Amirata Ghorbani, Abubakar Abid, James Zou
In order for machine learning to be deployed and trusted in many applications, it is crucial to be able to reliably explain why the machine learning algorithm makes certain predict…
Contrastive Principal Component Analysis
Abubakar Abid, Martin J. Zhang, Vivek K. Bagaria +1
We present a new technique called contrastive principal component analysis (cPCA) that is designed to discover low-dimensional structure that is unique to a dataset, or enriched in…
Why Adaptively Collected Data Have Negative Bias and How to Correct for It
Xinkun Nie, Xiaoying Tian, Jonathan Taylor +1
From scientific experiments to online A/B testing, the previously observed data often affects how future experiments are performed, which in turn affects which data will be collect…
Linear Regression with Shuffled Labels
Abubakar Abid, Ada Poon, James Zou
Is it possible to perform linear regression on datasets whose labels are shuffled with respect to the inputs? We explore this question by proposing several estimators that recover…
Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation
Akash Srivastava, James Zou, Ryan P. Adams +1
A good clustering can help a data analyst to explore and understand a data set, but what constitutes a good clustering may depend on domain-specific and application-specific criter…
Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation
Akash Srivastava, James Zou, Charles Sutton
A good clustering can help a data analyst to explore and understand a data set, but what constitutes a good clustering may depend on domain-specific and application-specific criter…
Crowdsourcing Feature Discovery via Adaptively Chosen Comparisons
James Y. Zou, Kamalika Chaudhuri, Adam Tauman Kalai
We introduce an unsupervised approach to efficiently discover the underlying features in a data set via crowdsourcing. Our queries ask crowd members to articulate a feature common…