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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
Advances in neural information processing systems 31, 2018
6622018
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
4202019
Predicting abnormal returns from news using text classification
R Luss, A d’Aspremont
Quantitative Finance 15 (6), 999-1012, 2015
1952015
Support vector machine classification with indefinite kernels
R Luss, A d'Aspremont
Advances in neural information processing systems 20, 2007
1772007
Conditional gradient algorithmsfor rank-one matrix approximations with a sparsity constraint
R Luss, M Teboulle
siam REVIEW 55 (1), 65-98, 2013
1212013
Ai explainability 360 toolkit
V Arya, RKE Bellamy, PY Chen, A Dhurandhar, M Hind, SC Hoffman, ...
Proceedings of the 3rd ACM India Joint International Conference on Data …, 2021
892021
Leveraging latent features for local explanations
R Luss, PY Chen, A Dhurandhar, P Sattigeri, Y Zhang, K Shanmugam, ...
Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021
78*2021
Efficient regularized isotonic regression with application to gene–gene interaction search
R Luss, S Rosset, M Shahar
712012
Clustering and feature selection using sparse principal component analysis
R Luss, A d’Aspremont
Optimization and Engineering 11, 145-157, 2010
712010
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
702019
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, ...
URL https://arxiv. org/abs, 1909
661909
Improving simple models with confidence profiles
A Dhurandhar, K Shanmugam, R Luss, PA Olsen
Advances in Neural Information Processing Systems 31, 2018
602018
Connecting algorithmic research and usage contexts: a perspective of contextualized evaluation for explainable AI
QV Liao, Y Zhang, R Luss, F Doshi-Velez, A Dhurandhar
Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 10 …, 2022
542022
Stochastic gradient descent with biased but consistent gradient estimators
J Chen, R Luss
arXiv preprint arXiv:1807.11880, 2018
492018
Tip: Typifying the interpretability of procedures
A Dhurandhar, V Iyengar, R Luss, K Shanmugam
arXiv preprint arXiv:1706.02952, 2017
432017
Generalized isotonic regression
R Luss, S Rosset
Journal of Computational and Graphical Statistics 23 (1), 192-210, 2014
332014
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
292014
A formal framework to characterize interpretability of procedures
A Dhurandhar, V Iyengar, R Luss, K Shanmugam
arXiv preprint arXiv:1707.03886, 2017
262017
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
232014
Sparse quantile huber regression for efficient and robust estimation
AY Aravkin, A Kambadur, AC Lozano, R Luss
arXiv preprint arXiv:1402.4624, 2014
192014
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