activity
20172023
most citedData Shapley: Equitable Valuation of Data for Machine Learning

152 citations · 659 across the 17 of their papers we have counts for

collaborators
Showing stat.MLShow all

7 papers · 1 filter

stat.ML2021

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…

stat.ML2021★ 5 cited

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…

stat.ML2020★ 64 cited

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…

stat.ML2019★ 152 cited

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…

stat.ML2019★ 86 cited

Towards Automatic Concept-based Explanations

Amirata Ghorbani, James Wexler, James Zou +1

Interpretability has become an important topic of research as more machine learning (ML) models are deployed and widely used to make important decisions. Most of the current explan…

stat.ML2018★ 20 cited

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…

stat.ML2017★ 72 cited

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…