• 2965 Citations
  • 17 h-Index
20072019
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Fingerprint Dive into the research topics where Mehrdad Mahdavi is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Convex optimization Engineering & Materials Science
Random Projection Mathematics
Clustering algorithms Engineering & Materials Science
Harmony Search Mathematics
Online Optimization Mathematics
Sketching Mathematics
High-dimensional Data Mathematics
Regret Mathematics

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Research Output 2007 2019

Learning Feature Nonlinearities with Regularized Binned Regression

Oymak, S., Mahdavi, M. & Chen, J., Jul 2019, 2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 1452-1456 5 p. 8849541. (IEEE International Symposium on Information Theory - Proceedings; vol. 2019-July).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Regression
Nonlinearity
Quantile
Regression Tree
Binning

Sketching meets random projection in the dual: A provable recovery algorithm for big and high-dimensional data

Wang, J., Lee, J. D., Mahdavi, M., Kolar, M. & Srebro, N., Jan 1 2017.

Research output: Contribution to conferencePaper

Random Projection
Sketching
High-dimensional Data
Recovery
Primal-dual Method
3 Citations (Scopus)

Sketching meets random projection in the dual: A provable recovery algorithm for big and high-dimensional data

Wang, J., Lee, J. D., Mahdavi, M., Kolar, M. & Srebro, N., Jan 1 2017, In : Electronic Journal of Statistics. 11, 2, p. 4896-4944 49 p.

Research output: Contribution to journalArticle

Open Access
Random Projection
Sketching
High-dimensional Data
Primal-dual
Recovery

Train and test tightness of lp relaxations in structured prediction

Meshi, O., Mahdavi, M., Weiler, A. & Sontag, D., Jan 1 2016, 33rd International Conference on Machine Learning, ICML 2016. Weinberger, K. Q. & Balcan, M. F. (eds.). International Machine Learning Society (IMLS), p. 2652-2664 13 p. (33rd International Conference on Machine Learning, ICML 2016; vol. 4).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Linear programming
Computer vision
Processing

Lower and upper bounds on the generalization of stochastic exponentially concave optimization

Mahdavi, M., Zhang, L. & Jin, R., Jan 1 2015, In : Journal of Machine Learning Research. 40, 2015

Research output: Contribution to journalConference article

Upper and Lower Bounds
Excess
Optimization
Concave function
Stochastic Optimization