| Explanations based on the missing: Towards contrastive explanations with pertinent negatives A Dhurandhar, PY Chen, R Luss, CC Tu, P Ting, K Shanmugam, P Das arXiv preprint arXiv:1802.07623, 2018 | 187 | 2018 |
| Support vector machine classification with indefinite kernels R Luss, A d’Aspremont Mathematical Programming Computation 1 (2), 97-118, 2009 | 145 | 2009 |
| Predicting abnormal returns from news using text classification R Luss, A d’Aspremont Quantitative Finance 15 (6), 999-1012, 2015 | 144 | 2015 |
| One explanation does not fit all: A toolkit and taxonomy of ai explainability techniques V Arya, RKE Bellamy, PY Chen, A Dhurandhar, M Hind, SC Hoffman, ... arXiv preprint arXiv:1909.03012, 2019 | 105 | 2019 |
| Conditional gradient algorithmsfor rank-one matrix approximations with a sparsity constraint R Luss, M Teboulle siam REVIEW 55 (1), 65-98, 2013 | 100 | 2013 |
| Clustering and feature selection using sparse principal component analysis R Luss, A d’Aspremont Optimization and Engineering 11 (1), 145-157, 2010 | 55 | 2010 |
| Efficient regularized isotonic regression with application to gene-gene interaction search R Luss, S Rosset, M Shahar The Annals of Applied Statistics, 253-283, 2012 | 49 | 2012 |
| Tip: Typifying the interpretability of procedures A Dhurandhar, V Iyengar, R Luss, K Shanmugam arXiv preprint arXiv:1706.02952, 2017 | 30 | 2017 |
| Beyond backprop: Online alternating minimization with auxiliary variables A Choromanska, B Cowen, S Kumaravel, R Luss, M Rigotti, I Rish, ... International Conference on Machine Learning, 1193-1202, 2019 | 27 | 2019 |
| Stochastic gradient descent with biased but consistent gradient estimators J Chen, R Luss arXiv preprint arXiv:1807.11880, 2018 | 26 | 2018 |
| A formal framework to characterize interpretability of procedures A Dhurandhar, V Iyengar, R Luss, K Shanmugam arXiv preprint arXiv:1707.03886, 2017 | 25 | 2017 |
| Improving simple models with confidence profiles A Dhurandhar, K Shanmugam, R Luss, P Olsen arXiv preprint arXiv:1807.07506, 2018 | 24 | 2018 |
| Generalized isotonic regression R Luss, S Rosset Journal of Computational and Graphical Statistics 23 (1), 192-210, 2014 | 23 | 2014 |
| Generating contrastive explanations with monotonic attribute functions R Luss, PY Chen, A Dhurandhar, P Sattigeri, Y Zhang, K Shanmugam, ... arXiv preprint arXiv:1905.12698, 2019 | 21 | 2019 |
| One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques.(2019) V Arya, RKE Bellamy, PY Chen, A Dhurandhar, M Hind, SC Hoffman, ... arXiv preprint arXiv:1909.03012, 1909 | 21 | 1909 |
| Orthogonal matching pursuit for sparse quantile regression A Aravkin, A Lozano, R Luss, P Kambadur 2014 IEEE international conference on data mining, 11-19, 2014 | 19 | 2014 |
| Decomposing isotonic regression for efficiently solving large problems R Luss, S Rosset, M Shahar Advances in neural information processing systems 23, 1513-1521, 2010 | 18 | 2010 |
| Convex approximations to sparse PCA via Lagrangian duality R Luss, M Teboulle Operations Research Letters 39 (1), 57-61, 2011 | 14 | 2011 |
| Social media and customer behavior analytics for personalized customer engagements S Buckley, M Ettl, P Jain, R Luss, M Petrik, RK Ravi, C Venkatramani IBM Journal of Research and Development 58 (5/6), 7: 1-7: 12, 2014 | 13 | 2014 |
| AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models. V Arya, RKE Bellamy, PY Chen, A Dhurandhar, M Hind, SC Hoffman, ... J. Mach. Learn. Res. 21 (130), 1-6, 2020 | 12 | 2020 |
