Reliable Machine Learning

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년 연구분야 출판 유형

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• 2022

  • Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals

    Saloni Dash, Vineeth Balasubramanian, Amit Sharma

    Winter Conference on Applications of Computer Vision (WACV) | January 2022

    PDF Publication

• 2021

  • Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End

    Ramaravind Kommiya Mothilal, Divyat Mahajan, Chenhao Tan, Amit Sharma

    AAAI/ACM Conference on AI, Ethics, and Society (AIES) | April 2021

    PDF Publication 프로젝트

  • The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

    Naman Goel, Alfonso Amayeulas, Amit Deshpande, Amit Sharma

    AAAI Conference on Artificial Intelligence (AAAI) | February 2021

    Publication PDF

• 2020

  • Inframarginality Audit of Group-Fairness

    Amit Sharma, Arpita Biswas, Siddharth Barman

    June 2020

    Publication

  • Explaining Machine Learning Classifiers through Diverse Counterfactual Examples

    Ramaravind K. Mothilal, Amit Sharma, Chenhao Tan

    ACM Conference on Fairness, Accountability, and Transparency | January 2020

    PDF Github 프로젝트

모두 펼치기 | 모두 축소

• Artificial intelligence

  • Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals

    Saloni Dash, Vineeth Balasubramanian, Amit Sharma

    Winter Conference on Applications of Computer Vision (WACV) | January 2022

    PDF Publication

  • Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End

    Ramaravind Kommiya Mothilal, Divyat Mahajan, Chenhao Tan, Amit Sharma

    AAAI/ACM Conference on AI, Ethics, and Society (AIES) | April 2021

    PDF Publication 프로젝트

  • The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

    Naman Goel, Alfonso Amayeulas, Amit Deshpande, Amit Sharma

    AAAI Conference on Artificial Intelligence (AAAI) | February 2021

    Publication PDF

  • Inframarginality Audit of Group-Fairness

    Amit Sharma, Arpita Biswas, Siddharth Barman

    June 2020

    Publication

  • Explaining Machine Learning Classifiers through Diverse Counterfactual Examples

    Ramaravind K. Mothilal, Amit Sharma, Chenhao Tan

    ACM Conference on Fairness, Accountability, and Transparency | January 2020

    PDF Github 프로젝트

모두 펼치기 | 모두 축소

• Inproceedings (Conference)

  • Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals

    Saloni Dash, Vineeth Balasubramanian, Amit Sharma

    Winter Conference on Applications of Computer Vision (WACV) | January 2022

    PDF Publication

  • Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End

    Ramaravind Kommiya Mothilal, Divyat Mahajan, Chenhao Tan, Amit Sharma

    AAAI/ACM Conference on AI, Ethics, and Society (AIES) | April 2021

    PDF Publication 프로젝트

  • The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

    Naman Goel, Alfonso Amayeulas, Amit Deshpande, Amit Sharma

    AAAI Conference on Artificial Intelligence (AAAI) | February 2021

    Publication PDF

  • Explaining Machine Learning Classifiers through Diverse Counterfactual Examples

    Ramaravind K. Mothilal, Amit Sharma, Chenhao Tan

    ACM Conference on Fairness, Accountability, and Transparency | January 2020

    PDF Github 프로젝트

• Miscellaneous

  • Inframarginality Audit of Group-Fairness

    Amit Sharma, Arpita Biswas, Siddharth Barman

    June 2020

    Publication