| Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods | 0 | 0.34 | 2022 |
| Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis | 0 | 0.34 | 2022 |
| Data poisoning attacks on off-policy policy evaluation methods. | 0 | 0.34 | 2022 |
| Reliable Post hoc Explanations: Modeling Uncertainty in Explainability. | 0 | 0.34 | 2021 |
| Towards Reliable and Practicable Algorithmic Recourse | 0 | 0.34 | 2021 |
| Fair Influence Maximization: A Welfare Optimization Approach | 0 | 0.34 | 2021 |
| Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring | 1 | 0.41 | 2021 |
| Towards The Unification And Robustness Of Perturbation And Gradient Based Explanations | 0 | 0.34 | 2021 |
| Towards a unified framework for fair and stable graph representation learning. | 0 | 0.34 | 2021 |
| Counterfactual Explanations Can Be Manipulated. | 0 | 0.34 | 2021 |
| Towards Robust and Reliable Algorithmic Recourse. | 0 | 0.34 | 2021 |
| Incorporating Interpretable Output Constraints in Bayesian Neural Networks | 0 | 0.34 | 2020 |
| "How do I fool you?" - Manipulating User Trust via Misleading Black Box Explanations. | 1 | 0.35 | 2020 |
| Robust and Stable Black Box Explanations | 0 | 0.34 | 2020 |
| Beyond Individualized Recourse - Interpretable and Interactive Summaries of Actionable Recourses. | 0 | 0.34 | 2020 |
| Fooling LIME and SHAP - Adversarial Attacks on Post hoc Explanation Methods. | 4 | 0.42 | 2020 |
| The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables | 9 | 0.59 | 2017 |
| Interpretable & Explorable Approximations of Black Box Models. | 12 | 0.56 | 2017 |
| Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration. | 14 | 0.69 | 2017 |
| Learning Cost-Effective Treatment Regimes using Markov Decision Processes. | 1 | 0.38 | 2016 |
| Interpretable Decision Sets: A Joint Framework for Description and Prediction | 57 | 2.18 | 2016 |
| Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making. | 0 | 0.34 | 2016 |
| Discovering Blind Spots of Predictive Models: Representations and Policies for Guided Exploration. | 1 | 0.37 | 2016 |
| Psycho-Demographic Analysis of the Facebook Rainbow Campaign. | 0 | 0.34 | 2016 |
| A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes | 16 | 1.09 | 2015 |
| Who, when, and why: a machine learning approach to prioritizing students at risk of not graduating high school on time | 6 | 0.57 | 2015 |
| A Bayesian Framework for Modeling Human Evaluations. | 5 | 0.51 | 2015 |
| What's in a Name? Understanding the Interplay between Titles, Content, and Communities in Social Media. | 45 | 1.55 | 2013 |
| TEM: a novel perspective to modeling content onmicroblogs | 0 | 0.34 | 2012 |
| Dynamic Multi-relational Chinese Restaurant Process for Analyzing Influences on Users in Social Media | 5 | 0.49 | 2012 |
| Exploiting Coherence for the Simultaneous Discovery of Latent Facets and associated Sentiments. | 35 | 1.22 | 2011 |
| Smart news feeds for social networks using scalable joint latent factor models | 3 | 0.51 | 2011 |
| Attention prediction on social media brand pages | 17 | 0.83 | 2011 |
