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Publications

Publications

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  • All HBS Web  (120,062)
    • Faculty Publications  (81)

    Show Results For

    • All HBS Web  (120,062)
      • Faculty Publications  (81)

      Lakkaraju, HimabinduRemove Lakkaraju, Himabindu →

      ← Page 3 of 81 Results →
      • 2020
      • Conference Presentation

      An Empirical Study of the Trade-Offs Between Interpretability and Fairness

      By: Shahin Jabbari, Han-Ching Ou, Himabindu Lakkaraju and Milind Tambe
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      Jabbari, Shahin, Han-Ching Ou, Himabindu Lakkaraju, and Milind Tambe. "An Empirical Study of the Trade-Offs Between Interpretability and Fairness." Paper presented at the ICML Workshop on Human Interpretability in Machine Learning, International Conference on Machine Learning (ICML), 2020.
      • Article

      Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses

      By: Kaivalya Rawal and Himabindu Lakkaraju
      As predictive models are increasingly being deployed in high-stakes decision-making, there has been a lot of interest in developing algorithms which can provide recourses to affected individuals. While developing such tools is important, it is even more critical to... View Details
      Keywords: Predictive Models; Decision Making; Framework; Mathematical Methods
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      Rawal, Kaivalya, and Himabindu Lakkaraju. "Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses." Advances in Neural Information Processing Systems (NeurIPS) 33 (2020).
      • Article

      Incorporating Interpretable Output Constraints in Bayesian Neural Networks

      By: Wanqian Yang, Lars Lorch, Moritz Graule, Himabindu Lakkaraju and Finale Doshi-Velez
      Domains where supervised models are deployed often come with task-specific constraints, such as prior expert knowledge on the ground-truth function, or desiderata like safety and fairness. We introduce a novel probabilistic framework for reasoning with such constraints... View Details
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      Yang, Wanqian, Lars Lorch, Moritz Graule, Himabindu Lakkaraju, and Finale Doshi-Velez. "Incorporating Interpretable Output Constraints in Bayesian Neural Networks." Advances in Neural Information Processing Systems (NeurIPS) 33 (2020).
      • Article

      Robust and Stable Black Box Explanations

      By: Himabindu Lakkaraju, Nino Arsov and Osbert Bastani
      As machine learning black boxes are increasingly being deployed in real-world applications, there has been a growing interest in developing post hoc explanations that summarize the behaviors of these black boxes. However, existing algorithms for generating such... View Details
      Keywords: Machine Learning; Black Box Models; Framework
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      Lakkaraju, Himabindu, Nino Arsov, and Osbert Bastani. "Robust and Stable Black Box Explanations." Proceedings of the International Conference on Machine Learning (ICML) 37th (2020): 5628–5638. (Published in PMLR, Vol. 119.)
      • 2020
      • Article

      'How Do I Fool You?': Manipulating User Trust via Misleading Black Box Explanations

      By: Himabindu Lakkaraju and Osbert Bastani
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      Lakkaraju, Himabindu, and Osbert Bastani. "'How Do I Fool You?': Manipulating User Trust via Misleading Black Box Explanations." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (2020): 79–85.
      • 2020
      • Article

      Fooling LIME and SHAP: Adversarial Attacks on Post Hoc Explanation Methods.

      By: Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh and Himabindu Lakkaraju
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      Slack, Dylan, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju. "Fooling LIME and SHAP: Adversarial Attacks on Post Hoc Explanation Methods." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (2020): 180–186.
      • Article

      Faithful and Customizable Explanations of Black Box Models

      By: Himabindu Lakkaraju, Ece Kamar, Rich Caruana and Jure Leskovec
      As predictive models increasingly assist human experts (e.g., doctors) in day-to-day decision making, it is crucial for experts to be able to explore and understand how such models behave in different feature subspaces in order to know if and when to trust them. To... View Details
      Keywords: Interpretable Machine Learning; Black Box Models; Decision Making; Framework
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      Lakkaraju, Himabindu, Ece Kamar, Rich Caruana, and Jure Leskovec. "Faithful and Customizable Explanations of Black Box Models." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (2019).
      • Article

      Human Decisions and Machine Predictions

      By: Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig and Sendhil Mullainathan
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      Kleinberg, Jon, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan. "Human Decisions and Machine Predictions." Quarterly Journal of Economics 133, no. 1 (February 2018): 237–293.
      • Article

      The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables

      By: Himabindu Lakkaraju, Jon Kleinberg, Jure Leskovec, Jens Ludwig and Sendhil Mullainathan
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      Lakkaraju, Himabindu, Jon Kleinberg, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan. "The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables." Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining 23rd (2017).
      • Article

      Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration

      By: Himabindu Lakkaraju, Ece Kamar, Rich Caruana and Eric Horvitz
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      Lakkaraju, Himabindu, Ece Kamar, Rich Caruana, and Eric Horvitz. "Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration." Proceedings of the AAAI Conference on Artificial Intelligence 31st (2017).
      • Article

      Learning Cost-Effective and Interpretable Treatment Regimes

      By: Himabindu Lakkaraju and Cynthia Rudin
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      Lakkaraju, Himabindu, and Cynthia Rudin. "Learning Cost-Effective and Interpretable Treatment Regimes." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 20th (2017).
      • 9 Dec 2016
      • Conference Presentation

      Discovering Unknown Unknowns of Predictive Models

      By: Himabindu Lakkaraju, Ece Kamar, Rich Caruana and Eric Horvitz
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      Lakkaraju, Himabindu, Ece Kamar, Rich Caruana, and Eric Horvitz. "Discovering Unknown Unknowns of Predictive Models." Paper presented at the 30th Annual Conference on Neural Information Processing Systems (NIPS), Workshop on Reliable Machine Learning in the Wild, Barcelona, Spain, December 9, 2016.
      • 9 Dec 2016
      • Conference Presentation

      Learning Cost-Effective and Interpretable Regimes for Treatment Recommendation

      By: Himabindu Lakkaraju and Cynthia Rudin
      Citation
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      Lakkaraju, Himabindu, and Cynthia Rudin. "Learning Cost-Effective and Interpretable Regimes for Treatment Recommendation." Paper presented at the 30th Annual Conference on Neural Information Processing Systems (NIPS), Workshop on Interpretable Machine Learning in Complex Systems, Barcelona, Spain, December 9, 2016.
      • 8 Dec 2016
      • Conference Presentation

      Learning Cost-Effective and Interpretable Treatment Regimes for Judicial Bail Decisions

      By: Himabindu Lakkaraju and Cynthia Rudin
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      Lakkaraju, Himabindu, and Cynthia Rudin. "Learning Cost-Effective and Interpretable Treatment Regimes for Judicial Bail Decisions." Paper presented at the 30th Annual Conference on Neural Information Processing Systems (NIPS), Symposium on Machine Learning and the Law, Barcelona, Spain, December 8, 2016.
      • 2016
      • Article

      Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making

      By: Himabindu Lakkaraju and Jure Leskovec
      Citation
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      Lakkaraju, Himabindu, and Jure Leskovec. "Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making." Advances in Neural Information Processing Systems (NeurIPS) 29 (2016).
      • Article

      Interpretable Decision Sets: A Joint Framework for Description and Prediction

      By: Himabindu Lakkaraju, Stephen H. Bach and Jure Leskovec
      Citation
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      Lakkaraju, Himabindu, Stephen H. Bach, and Jure Leskovec. "Interpretable Decision Sets: A Joint Framework for Description and Prediction." Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining 22nd (2016).
      • Article

      Mining Big Data to Extract Patterns and Predict Real-Life Outcomes

      By: Michal Kosinki, Yilun Wang, Himabindu Lakkaraju and Jure Leskovec
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      Kosinki, Michal, Yilun Wang, Himabindu Lakkaraju, and Jure Leskovec. "Mining Big Data to Extract Patterns and Predict Real-Life Outcomes." Psychological Methods 21, no. 4 (December 2016): 493–506.
      • 2015
      • Article

      A Bayesian Framework for Modeling Human Evaluations

      By: Himabindu Lakkaraju, Jure Leskovec, Jon Kleinberg and Sendhil Mullainathan
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      Lakkaraju, Himabindu, Jure Leskovec, Jon Kleinberg, and Sendhil Mullainathan. "A Bayesian Framework for Modeling Human Evaluations." Proceedings of the SIAM International Conference on Data Mining (2015): 181–189.
      • 2015
      • Article

      A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes

      By: Himabindu Lakkaraju, Everaldo Aguiar, Carl Shan, David Miller, Nasir Bhanpuri, Rayid Ghani and Kecia Addison
      Citation
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      Lakkaraju, Himabindu, Everaldo Aguiar, Carl Shan, David Miller, Nasir Bhanpuri, Rayid Ghani, and Kecia Addison. "A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes." Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining 21st (2015).
      • Article

      Who, When, and Why: A Machine Learning Approach to Prioritizing Students at Risk of Not Graduating High School on Time

      By: Everaldo Aguiar, Himabindu Lakkaraju, Nasir Bhanpuri, David Miller, Ben Yuhas, Kecia Addison and Rayid Ghani
      Citation
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      Aguiar, Everaldo, Himabindu Lakkaraju, Nasir Bhanpuri, David Miller, Ben Yuhas, Kecia Addison, and Rayid Ghani. "Who, When, and Why: A Machine Learning Approach to Prioritizing Students at Risk of Not Graduating High School on Time." Proceedings of the International Learning Analytics and Knowledge Conference 5th (2015).
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