2022:
2022:
Embedded Files
Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials
Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials
Andre Esteva, Jean Feng, Douwe van der Wal, ..., Amirata Ghorbani, et al.
Nature Digital Medicine
June 2022
[Paper]
Determining a patient's optimal therapy using a multi-modal deep learning architecture.
2021:
2021:
Who’s responsible? Jointly quantifying the contribution of the learning algorithm and data
Who’s responsible? Jointly quantifying the contribution of the learning algorithm and data
Gal Yona, Amirata Ghorbani, James Zou
AAAI/ACM Conference on AI, Ethics, and Society (AIES)
May 2021
[Paper]
Introducing a new algorithm for simeltenous valuation of data and model
Data valuation for medical imaging using Shapley value and application to a large‑scale chest X‑ray dataset
Data valuation for medical imaging using Shapley value and application to a large‑scale chest X‑ray dataset
Siyi Tang, Amirata Ghorbani, Rikiya Yamashita, Sameer Rehman, Jared A. Dunnmon, James Zou, Daniel L. Rubin
Nature Scientific Reports
Apr 2021
[Paper]
How Does Mixup Help With Robustness and Generalization
How Does Mixup Help With Robustness and Generalization
Linjun Zhang*, Zhun Deng*, Kenji Kawaguchi*, Amirata Ghorbani, James Zou
International Conference on Learning Representations (ICLR)
May 2021
[Paper]
A theoretical point of view for Mixup training
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data
Zhun Deng*, Linjun Zhang*, Amirata Ghorbani, James Zou
International Conference on Artificial Intelligence and Statistics (AISTATS)
Apr 2021
[Paper]
We show with theoretical and empirical analysis that robustness of machine learning models against adversarial examples can be improved by adding cheap unlabeled data.
2020:
2020:
Neuron Shapley:
Neuron Shapley:
Discovering the Responsible Neurons
Discovering the Responsible Neurons
Distributional Shapley:
Distributional Shapley:
A Distributional Framework for Data Valuation
A Distributional Framework for Data Valuation
Amirata Ghorbani*, Michael P. Kim*, James Zou (*equal contribution)
International Conference in Machine Learning (ICML)
Jul 2020
[Paper] [Code] [Slides][Presentation]
We propose a the distributional Shapley framework where the value of a data point is defined in the context of an underlying data distribution. We demonstrate the utility of this approach in a data market setting.
EchoNet-Dynamic:
EchoNet-Dynamic:
Video-based AI for beat-to-beat assessment of cardiac function
Video-based AI for beat-to-beat assessment of cardiac function
EchoNet:
EchoNet:
Deep Learning Interpretation of Echocardiograms
Deep Learning Interpretation of Echocardiograms
Amirata Ghorbani*, David Ouyang* , et al. (*equal contribution)
Nature Digital Medicine
Jan 2020
[Paper] [Slides] [Presentation]
Using CNNs with a large new dataset, we show that deep learning applied to echocardiography can identify local cardiac structures, estimate cardiac function, and predict systemic phenotypes.
2019:
2019:
DermGan:
DermGan:
Synthetic Generation of Clinical Skin Images with Pathology
Synthetic Generation of Clinical Skin Images with Pathology
ACE:
ACE:
Towards Automatic Concept-based Explanations
Towards Automatic Concept-based Explanations
Amirata Ghorbani, James Wexler, James Zou, Been Kim
Neural Information Processing Systems (NeurIPS)
Dec 2019 (Poster Presentation)
[Paper] [Code] [Slides] [Poster]
We develop a new algorithm, ACE, to interpret deep convolutional neural networks. ACE is able to automatically extract visual concepts that are human-meaningful, coherent and important and use them to explain the neural network’s predictions.
Data Shapley:
Data Shapley:
Equitable Valuation of Data for Machine Learning
Equitable Valuation of Data for Machine Learning
Amirata Ghorbani, James Zou
International Conference in Machine Learning (ICML)
Jun 2019 (Long Oral Presentation)
[Paper] [Code] [Slides] [Presentation] [Poster]
We propose efficient algorithms for estimating the value of data points or data sources for the supervised machine learning problem.
Multiaccuracy:
Multiaccuracy:
Black-box post-processing for fairness in classification
Black-box post-processing for fairness in classification
Michael P Kim*, Amirata Ghorbani*, James Zou (* equal contribution)
AAAI/ACM Conference on AI, Ethics, and Society (AIES)
Feb 2019 (Spotlight Presentation)
[Paper] [Code] [Slides] [Poster]
We develop a rigorous framework of multiaccuracy auditing and post-processing to improve predictor accuracy across identifiable subgroups.
Knockoffs for the mass: New feature importance statistics with false discovery guarantees
Knockoffs for the mass: New feature importance statistics with false discovery guarantees
Jaime Roquero Gimenez, Amirata Ghorbani, James Zou
International Conference on Artificial Intelligence and Statistics (AISTATS)
Apr 2019 (Poster Presentation)
[Paper]
We develop an efficient algorithm to generate valid knockoffs from Bayesian Networks. Then we systematically evaluate knockoff test statistics and develop new statistics with improved power.
Interpretation of Neural Network is Fragile
Interpretation of Neural Network is Fragile
Amirata Ghorbani*, Abubakar Abid*, James Zou (* equal contribution)
AAAI Conference on Artificial Intelligence (AAAI)
Feb 2019 (Oral Presentation)
[Paper] [Code] [Slides] [Presentation]
We demonstrate how to generate adversarial perturbations that produce perceptively indistinguishable inputs that are assigned the same predicted label, yet have very different interpretations
2018:
2018:
2017:
2017:
Why Are Deep Networks Fragile:
Why Are Deep Networks Fragile:
Deformation of Intertwined Data
Deformation of Intertwined Data
Amirata Ghorbani, James Zou
International Conference on Machine Learning, Reliable Machine Learning in the Wild Workshop
Aug 2017 (Oral Presentation)
[Paper] [Presentation] [Poster]
We quantify how fitting an intertwined dataset requires the model to deform the original space of the datasets in a way that small perturbations can result in big changes in the model’s output.
Blind Iterative Nonlinear Distortion Compensation Based on Thresholding
Blind Iterative Nonlinear Distortion Compensation Based on Thresholding
Masoumeh Azghani, Amirata Ghorbani, and Farokh Marvasti
IEEE Transactions on Circuits and Systems II
[Paper]
The sampling process in electrical devices includes nonlinear distortion that needs to be compensated to boost up the system efficiency. In this brief, a blind method is suggested for nonlinear distortion compensation.
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