Himabindu Lakkaraju

According to our database1, Himabindu Lakkaraju authored at least 42 papers between 2011 and 2021.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Other 

Links

On csauthors.net:

Bibliography

2021
What will it take to generate fairness-preserving explanations?
CoRR, 2021

Feature Attributions and Counterfactual Explanations Can Be Manipulated.
CoRR, 2021

On the Connections between Counterfactual Explanations and Adversarial Examples.
CoRR, 2021

Towards a Rigorous Theoretical Analysis and Evaluation of GNN Explanations.
CoRR, 2021

Counterfactual Explanations Can Be Manipulated.
CoRR, 2021

Learning Under Adversarial and Interventional Shifts.
CoRR, 2021

Towards Robust and Reliable Algorithmic Recourse.
CoRR, 2021

Towards a Unified Framework for Fair and Stable Graph Representation Learning.
CoRR, 2021

Towards the Unification and Robustness of Perturbation and Gradient Based Explanations.
Proceedings of the 38th International Conference on Machine Learning, 2021

Fair Influence Maximization: a Welfare Optimization Approach.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Can I Still Trust You?: Understanding the Impact of Distribution Shifts on Algorithmic Recourses.
CoRR, 2020

Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring.
CoRR, 2020

When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making.
CoRR, 2020

Ensuring Actionable Recourse via Adversarial Training.
CoRR, 2020

Interpretable and Interactive Summaries of Actionable Recourses.
CoRR, 2020

How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations.
CoRR, 2020

Fair Influence Maximization: A Welfare Optimization Approach.
CoRR, 2020

Incorporating Interpretable Output Constraints in Bayesian Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Robust and Stable Black Box Explanations.
Proceedings of the 37th International Conference on Machine Learning, 2020

Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods.
Proceedings of the AIES '20: AAAI/ACM Conference on AI, 2020

"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations.
Proceedings of the AIES '20: AAAI/ACM Conference on AI, 2020

2019
How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods.
CoRR, 2019

Faithful and Customizable Explanations of Black Box Models.
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

2017
Interpretable & Explorable Approximations of Black Box Models.
CoRR, 2017

The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables.
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

Learning Cost-Effective and Interpretable Treatment Regimes.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
Psycho-Demographic Analysis of the Facebook Rainbow Campaign.
CoRR, 2016

Learning Cost-Effective Treatment Regimes using Markov Decision Processes.
CoRR, 2016

Discovering Blind Spots of Predictive Models: Representations and Policies for Guided Exploration.
CoRR, 2016

Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Interpretable Decision Sets: A Joint Framework for Description and Prediction.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

2015
A Bayesian Framework for Modeling Human Evaluations.
Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, BC, Canada, April 30, 2015

Who, when, and why: a machine learning approach to prioritizing students at risk of not graduating high school on time.
Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, 2015

A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes.
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

2013
What's in a Name? Understanding the Interplay between Titles, Content, and Communities in Social Media.
Proceedings of the Seventh International Conference on Weblogs and Social Media, 2013

2012
TEM: a novel perspective to modeling content onmicroblogs.
Proceedings of the 21st World Wide Web Conference, 2012

Dynamic Multi-relational Chinese Restaurant Process for Analyzing Influences on Users in Social Media.
Proceedings of the 12th IEEE International Conference on Data Mining, 2012

2011
Smart news feeds for social networks using scalable joint latent factor models.
Proceedings of the 20th International Conference on World Wide Web, 2011

Exploiting Coherence for the Simultaneous Discovery of Latent Facets and associated Sentiments.
Proceedings of the Eleventh SIAM International Conference on Data Mining, 2011

Attention prediction on social media brand pages.
Proceedings of the 20th ACM Conference on Information and Knowledge Management, 2011


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