Ronny Luss
Ronny Luss
IBM Research
Verified email at us.ibm.com
Title
Cited by
Cited by
Year
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
1872018
Support vector machine classification with indefinite kernels
R Luss, A d’Aspremont
Mathematical Programming Computation 1 (2), 97-118, 2009
1452009
Predicting abnormal returns from news using text classification
R Luss, A d’Aspremont
Quantitative Finance 15 (6), 999-1012, 2015
1442015
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
1052019
Conditional gradient algorithmsfor rank-one matrix approximations with a sparsity constraint
R Luss, M Teboulle
siam REVIEW 55 (1), 65-98, 2013
1002013
Clustering and feature selection using sparse principal component analysis
R Luss, A d’Aspremont
Optimization and Engineering 11 (1), 145-157, 2010
552010
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
492012
Tip: Typifying the interpretability of procedures
A Dhurandhar, V Iyengar, R Luss, K Shanmugam
arXiv preprint arXiv:1706.02952, 2017
302017
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
272019
Stochastic gradient descent with biased but consistent gradient estimators
J Chen, R Luss
arXiv preprint arXiv:1807.11880, 2018
262018
A formal framework to characterize interpretability of procedures
A Dhurandhar, V Iyengar, R Luss, K Shanmugam
arXiv preprint arXiv:1707.03886, 2017
252017
Improving simple models with confidence profiles
A Dhurandhar, K Shanmugam, R Luss, P Olsen
arXiv preprint arXiv:1807.07506, 2018
242018
Generalized isotonic regression
R Luss, S Rosset
Journal of Computational and Graphical Statistics 23 (1), 192-210, 2014
232014
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
212019
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
211909
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
192014
Decomposing isotonic regression for efficiently solving large problems
R Luss, S Rosset, M Shahar
Advances in neural information processing systems 23, 1513-1521, 2010
182010
Convex approximations to sparse PCA via Lagrangian duality
R Luss, M Teboulle
Operations Research Letters 39 (1), 57-61, 2011
142011
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
132014
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
122020
The system can't perform the operation now. Try again later.
Articles 1–20