| Support vector machine classification with indefinite kernels R Luss, A d’Aspremont Mathematical Programming Computation 1 (2), 97-118, 2009 | 139 | 2009 |
| 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 | 132 | 2018 |
| Predicting abnormal returns from news using text classification R Luss, A d’Aspremont Quantitative Finance 15 (6), 999-1012, 2015 | 129 | 2015 |
| Conditional gradient algorithmsfor rank-one matrix approximations with a sparsity constraint R Luss, M Teboulle siam REVIEW 55 (1), 65-98, 2013 | 94 | 2013 |
| 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 | 61 | 2019 |
| Clustering and feature selection using sparse principal component analysis R Luss, A d’Aspremont Optimization and Engineering 11 (1), 145-157, 2010 | 54 | 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 | 48 | 2012 |
| Tip: Typifying the interpretability of procedures A Dhurandhar, V Iyengar, R Luss, K Shanmugam arXiv preprint arXiv:1706.02952, 2017 | 26 | 2017 |
| Improving simple models with confidence profiles A Dhurandhar, K Shanmugam, R Luss, P Olsen arXiv preprint arXiv:1807.07506, 2018 | 25 | 2018 |
| A formal framework to characterize interpretability of procedures A Dhurandhar, V Iyengar, R Luss, K Shanmugam arXiv preprint arXiv:1707.03886, 2017 | 22 | 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 | 21 | 2019 |
| Generalized isotonic regression R Luss, S Rosset Journal of Computational and Graphical Statistics 23 (1), 192-210, 2014 | 21 | 2014 |
| Stochastic gradient descent with biased but consistent gradient estimators J Chen, R Luss arXiv preprint arXiv:1807.11880, 2018 | 20 | 2018 |
| 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 | 18 | 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 | 16 | 2010 |
| 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 | 15 | 1909 |
| 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 | 12 | 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 | 11 | 2019 |
| Beyond backprop: Alternating minimization with co-activation memory A Choromanska, E Tandon, S Kumaravel, R Luss, I Rish, B Kingsbury, ... stat 1050, 24, 2018 | 10 | 2018 |
