Amirata Ghorbani | DeepAI

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∙ 07/20/2022

DataPerf: Benchmarks for Data-Centric AI Development

Machine learning (ML) research has generally focused on models, while th...

17 Mark Mazumder, et al. ∙

∙ 11/10/2021

Beyond Importance Scores: Interpreting Tabular ML by Visualizing Feature Semantics

Interpretability is becoming an active research topic as machine learnin...

1 Amirata Ghorbani, et al. ∙

∙ 04/16/2021

Data Shapley Valuation for Efficient Batch Active Learning

Annotating the right set of data amongst all available data points is a ...

8 Amirata Ghorbani, et al. ∙

∙ 10/15/2020

Data Valuation for Medical Imaging Using Shapley Value: Application on A Large-scale Chest X-ray Dataset

The reliability of machine learning models can be compromised when train...

41 Siyi Tang, et al. ∙

∙ 10/09/2020

How Does Mixup Help With Robustness and Generalization?

Mixup is a popular data augmentation technique based on taking convex co...

7 Linjun Zhang, et al. ∙

∙ 06/15/2020

Improving Adversarial Robustness via Unlabeled Out-of-Domain Data

Data augmentation by incorporating cheap unlabeled data from multiple do...

0 Zhun Deng, et al. ∙

∙ 02/27/2020

A Distributional Framework for Data Valuation

Shapley value is a classic notion from game theory, historically used to...

7 Amirata Ghorbani, et al. ∙

∙ 02/23/2020

Neuron Shapley: Discovering the Responsible Neurons

We develop Neuron Shapley as a new framework to quantify the contributio...

14 Amirata Ghorbani, et al. ∙

∙ 11/20/2019

DermGAN: Synthetic Generation of Clinical Skin Images with Pathology

Despite the recent success in applying supervised deep learning to medic...

66 Amirata Ghorbani, et al. ∙

∙ 10/09/2019

Who's responsible? Jointly quantifying the contribution of the learning algorithm and training data

A fancy learning algorithm A outperforms a baseline method B when they a...

0 Gal Yona, et al. ∙

∙ 04/05/2019

Data Shapley: Equitable Valuation of Data for Machine Learning

As data becomes the fuel driving technological and economic growth, a fu...

0 Amirata Ghorbani, et al. ∙

∙ 02/07/2019

Automating Interpretability: Discovering and Testing Visual Concepts Learned by Neural Networks

Interpretability has become an important topic of research as more machi...

40 Amirata Ghorbani, et al. ∙

∙ 07/17/2018

Knockoffs for the mass: new feature importance statistics with false discovery guarantees

An important problem in machine learning and statistics is to identify f...

0 Jaime Roquero Gimenez, et al. ∙

∙ 05/31/2018

Multiaccuracy: Black-Box Post-Processing for Fairness in Classification

Machine learning predictors are successfully deployed in applications ra...

0 Michael P. Kim, et al. ∙

∙ 10/29/2017

Interpretation of Neural Networks is Fragile

In order for machine learning to be deployed and trusted in many applica...

0 Amirata Ghorbani, et al. ∙

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