Publications
Publications
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- All HBS Web
(117,452)
- Faculty Publications (80)
- 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
- 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
- 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
- 2020
- Article
'How Do I Fool You?': Manipulating User Trust via Misleading Black Box Explanations
By: Himabindu Lakkaraju and Osbert Bastani
- 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
- 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
- Article
Human Decisions and Machine Predictions
By: Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig and Sendhil Mullainathan
- 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
- Article
Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration
By: Himabindu Lakkaraju, Ece Kamar, Rich Caruana and Eric Horvitz
- Article
Learning Cost-Effective and Interpretable Treatment Regimes
By: Himabindu Lakkaraju and Cynthia Rudin
- 9 Dec 2016
- Conference Presentation
Discovering Unknown Unknowns of Predictive Models
By: Himabindu Lakkaraju, Ece Kamar, Rich Caruana and Eric Horvitz
- 9 Dec 2016
- Conference Presentation
Learning Cost-Effective and Interpretable Regimes for Treatment Recommendation
By: Himabindu Lakkaraju and Cynthia Rudin
- 8 Dec 2016
- Conference Presentation
Learning Cost-Effective and Interpretable Treatment Regimes for Judicial Bail Decisions
By: Himabindu Lakkaraju and Cynthia Rudin
- 2016
- Article
Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making
By: Himabindu Lakkaraju and Jure Leskovec
- Article
Interpretable Decision Sets: A Joint Framework for Description and Prediction
By: Himabindu Lakkaraju, Stephen H. Bach and Jure Leskovec
- Article
Mining Big Data to Extract Patterns and Predict Real-Life Outcomes
By: Michal Kosinki, Yilun Wang, Himabindu Lakkaraju and Jure Leskovec
- 2015
- Article
A Bayesian Framework for Modeling Human Evaluations
By: Himabindu Lakkaraju, Jure Leskovec, Jon Kleinberg and Sendhil Mullainathan
- 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
- 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
- 12 Dec 2014
- Conference Presentation
Aspect Specific Sentiment Analysis Using Hierarchical Deep Learning
By: Himabindu Lakkaraju, Richard Socher and Chris Manning