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Himabindu Lakkaraju
When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making

Nov 13, 2020
Sean McGrath, Parth Mehta, Alexandra Zytek, Isaac Lage, Himabindu Lakkaraju


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Robust and Stable Black Box Explanations

Nov 12, 2020
Himabindu Lakkaraju, Nino Arsov, Osbert Bastani


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Ensuring Actionable Recourse via Adversarial Training

Nov 12, 2020
Alexis Ross, Himabindu Lakkaraju, Osbert Bastani


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Incorporating Interpretable Output Constraints in Bayesian Neural Networks

Oct 21, 2020
Wanqian Yang, Lars Lorch, Moritz A. Graule, Himabindu Lakkaraju, Finale Doshi-Velez

* 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada. Code available at: https://github.com/dtak/ocbnn-public 
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Interpretable and Interactive Summaries of Actionable Recourses

Sep 16, 2020
Kaivalya Rawal, Himabindu Lakkaraju


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How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations

Aug 11, 2020
Dylan Slack, Sophie Hilgard, Sameer Singh, Himabindu Lakkaraju


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Fair Influence Maximization: A Welfare Optimization Approach

Jun 14, 2020
Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Eric Rice, Milind Tambe


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"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations

Nov 15, 2019
Himabindu Lakkaraju, Osbert Bastani


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How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods

Nov 06, 2019
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju


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Interpretable & Explorable Approximations of Black Box Models

Jul 04, 2017
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Jure Leskovec

* Presented as a poster at the 2017 Workshop on Fairness, Accountability, and Transparency in Machine Learning 
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Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration

Dec 10, 2016
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz

* To appear in AAAI 2017; Presented at NIPS Workshop on Reliability in ML, 2016 
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Learning Cost-Effective and Interpretable Regimes for Treatment Recommendation

Nov 23, 2016
Himabindu Lakkaraju, Cynthia Rudin

* Presented at NIPS 2016 Workshop on Interpretable Machine Learning in Complex Systems 
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Learning Cost-Effective Treatment Regimes using Markov Decision Processes

Oct 21, 2016
Himabindu Lakkaraju, Cynthia Rudin


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Dynamic Multi-Relational Chinese Restaurant Process for Analyzing Influences on Users in Social Media

May 07, 2012
Himabindu Lakkaraju, Indrajit Bhattacharya, Chiranjib Bhattacharyya

* 9 pages 
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