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- 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
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.
- 2020
- Article
"How Do I Fool You?": Manipulating User Trust via Misleading Black Box Explanations
By: Himabindu Lakkaraju and Osbert Bastani
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.
- Article
Human Decisions and Machine Predictions
By: Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig and Sendhil Mullainathan
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
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
Learning Cost-Effective and Interpretable Treatment Regimes
By: Himabindu Lakkaraju and Cynthia Rudin
Lakkaraju, Himabindu, and Cynthia Rudin. "Learning Cost-Effective and Interpretable Treatment Regimes." Proceedings of the International Conference on Artificial Intelligence and Statistics 20th (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
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).
- 9 Dec 2016
- Conference Presentation
Discovering Unknown Unknowns of Predictive Models
By: Himabindu Lakkaraju, Ece Kamar, Rich Caruana and Eric Horvitz
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
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
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.
- Article
Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making
By: Himabindu Lakkaraju and Jure Leskovec
Lakkaraju, Himabindu, and Jure Leskovec. "Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making." Proceedings of the Conference on Neural Information Processing Systems 30th (2016).
- Article
Interpretable Decision Sets: A Joint Framework for Description and Prediction
By: Himabindu Lakkaraju, Stephen H. Bach and Jure Leskovec
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
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 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
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).
- 2015
- Article
A Bayesian Framework for Modeling Human Evaluations
By: Himabindu Lakkaraju, Jure Leskovec, Jon Kleinberg and Sendhil Mullainathan
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.
- 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
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).
- 12 Dec 2014
- Conference Presentation
Aspect Specific Sentiment Analysis Using Hierarchical Deep Learning
By: Himabindu Lakkaraju, Richard Socher and Chris Manning
Lakkaraju, Himabindu, Richard Socher, and Chris Manning. "Aspect Specific Sentiment Analysis Using Hierarchical Deep Learning." Paper presented at the 28th Annual Conference on Neural Information Processing Systems (NIPS), Workshop on Deep Learning and Representation Learning, Montreal, Canada, December 12, 2014.
- 2014
- Other Unpublished Work
Using Big Data to Improve Social Policy
By: Himabindu Lakkaraju, Jon Kleinberg, Jure Leskovec, Jens Ludwig and Sendhil Mullainathan
- Article
What's in a Name? Understanding the Interplay Between Titles, Content, and Communities in Social Media
By: Himabindu Lakkaraju, Julian McAuley and Jure Leskovec
Lakkaraju, Himabindu, Julian McAuley, and Jure Leskovec. "What's in a Name? Understanding the Interplay Between Titles, Content, and Communities in Social Media." Proceedings of the International AAAI Conference on Weblogs and Social Media 7th (2013).
- Article
Dynamic Multi-Relational Chinese Restaurant Process for Analyzing Influences on Users in Social Media
By: Himabindu Lakkaraju, Indrajit Bhattacharya and Chiranjib Bhattacharyya
Lakkaraju, Himabindu, Indrajit Bhattacharya, and Chiranjib Bhattacharyya. "Dynamic Multi-Relational Chinese Restaurant Process for Analyzing Influences on Users in Social Media." Proceedings of the IEEE International Conference on Data Mining 12th (2012).
- Article
TEM: A Novel Perspective to Modeling Content on Microblogs
By: Himabindu Lakkaraju and Hyung-Il Ahn
Lakkaraju, Himabindu, and Hyung-Il Ahn. "TEM: A Novel Perspective to Modeling Content on Microblogs." Proceedings of the International World Wide Web Conference 21st (2012).