Shayak Sen

Computer Science · Physical Sciences

12h-index1.2kcitations25works0.02yr avg
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Research Topics

Privacy-Preserving Technologies in Data(7), Explainable Artificial Intelligence (XAI)(7), Ethics and Social Impacts of AI(4), Adversarial Robustness in Machine Learning(4), Machine Learning and Data Classification(4)

Publications25 total

Shayak Sen
Authors group·2022
Machine Learning Explainability and Robustness
2021· 15 cited
SoK: Differential Privacy as a Causal Property
2020· 29 citedOpen Access
Machine Learning Explainability in Finance: An Application to Default Risk Analysis
SSRN Electronic Journal·2019· 152 citedOpen Access
Machine learning explainability in finance: an application to default risk analysis
RePEc: Research Papers in Economics·2019· 13 cited
Feature-Wise Bias Amplification
arXiv (Cornell University)·2018· 10 citedOpen Access
Influence-Directed Explanations for Deep Convolutional Networks
2018· 55 cited
Correspondences between Privacy and Nondiscrimination: Why They Should\n Be Studied Together
arXiv (Cornell University)·2018Open Access
Correspondences between Privacy and Nondiscrimination: Why They Should Be Studied Together
arXiv (Cornell University)·2018· 3 citedOpen Access
Supervising Feature Influence
arXiv (Cornell University)·2018· 6 citedOpen Access
Influence-Directed Explanations for Deep Convolutional Networks
arXiv (Cornell University)·2018· 6 citedOpen Access
Latent Factor Interpretations for Collaborative Filtering
arXiv (Cornell University)·2017· 4 citedOpen Access
Use Privacy in Data-Driven Systems
2017· 58 cited
Differential Privacy as a Causal Property
arXiv (Cornell University)·2017· 2 citedOpen Access
Case Study: Explaining Diabetic Retinopathy Detection Deep CNNs via Integrated Gradients
arXiv (Cornell University)·2017· 1 citedOpen Access
Bias In, Bias Out? Evaluating the Folk Wisdom
DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)·2017· 40 citedOpen Access
Use Privacy in Data-Driven Systems: Theory and Experiments with Machine Learnt Programs
arXiv (Cornell University)·2017· 6 citedOpen Access
Algorithmic Transparency via Quantitative Input Influence
Studies in big data·2017· 42 cited
Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems
2016· 679 cited
Debugging Machine Learning Tasks
arXiv (Cornell University)·2016· 24 citedOpen Access
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Frequent Co-authors

Anupam Datta(23), Matt Fredrikson(9), Piotr Mardziel(5), K. Rustan M. Leino(4), Michael Carl Tschantz(4), Gihyuk Ko(3), Limin Jia(3), Yair Zick(2), Philippe Bracke(2), Carsten Jung(2), Sriram K. Rajamani(2), Linyi Li(2), Deepak Garg(2), Saikat Guha(1), Janice Tsai(1), Jeannette M. Wing(1), Aleksandar Chakarov(1), Aditya V. Nori(1), Deepak Vijaykeerthy(1), Kaiji Lu(1)