Ronny Luss
Ronny Luss
IBM Research
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Support vector machine classification with indefinite kernels
R Luss, A d’Aspremont
Mathematical Programming Computation 1 (2), 97-118, 2009
Predicting abnormal returns from news using text classification
R Luss, A d’Aspremont
Quantitative Finance 15 (6), 999-1012, 2015
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, 592-603, 2018
Conditional gradient algorithmsfor rank-one matrix approximations with a sparsity constraint
R Luss, M Teboulle
siam REVIEW 55 (1), 65-98, 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
Clustering and feature selection using sparse principal component analysis
R Luss, A d’Aspremont
Optimization and Engineering 11 (1), 145-157, 2010
Efficient regularized isotonic regression with application to gene–gene interaction search
R Luss, S Rosset, M Shahar
The Annals of Applied Statistics 6 (1), 253-283, 2012
Improving simple models with confidence profiles
A Dhurandhar, K Shanmugam, R Luss, PA Olsen
Advances in Neural Information Processing Systems 31, 10296-10306, 2018
Tip: Typifying the interpretability of procedures
A Dhurandhar, V Iyengar, R Luss, K Shanmugam
arXiv preprint arXiv:1706.02952, 2017
Generalized isotonic regression
R Luss, S Rosset
Journal of Computational and Graphical Statistics 23 (1), 192-210, 2014
A formal framework to characterize interpretability of procedures
A Dhurandhar, V Iyengar, R Luss, K Shanmugam
arXiv preprint arXiv:1707.03886, 2017
Stochastic gradient descent with biased but consistent gradient estimators
J Chen, R Luss
arXiv preprint arXiv:1807.11880, 2018
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
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
Decomposing isotonic regression for efficiently solving large problems
R Luss, S Rosset, M Shahar
Advances in neural information processing systems, 1513-1521, 2010
Convex approximations to sparse PCA via Lagrangian duality
R Luss, M Teboulle
Operations Research Letters 39 (1), 57-61, 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
Beyond backprop: Alternating minimization with co-activation memory
A Choromanska, E Tandon, S Kumaravel, R Luss, I Rish, B Kingsbury, ...
stat 1050, 24, 2018
Analysis of social media messages
SJ Buckley, MR Ettl, MO Frey, P Jain, R Luss, M Petrik, RK Ravi, ...
US Patent App. 14/496,060, 2016
Sparse quantile huber regression for efficient and robust estimation
AY Aravkin, A Kambadur, AC Lozano, R Luss
arXiv preprint arXiv:1402.4624, 2014
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