Nati Srebro, Professor - Publications

Affiliations: 
CS TTI 
Area:
Machine learning

62 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2016 Needell D, Srebro N, Ward R. Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm Mathematical Programming. 155: 549-573. DOI: 10.1007/s10107-015-0864-7  0.88
2015 Meshi O, Srebro N, Hazan T. Efficient training of structured SVMs via soft constraints Journal of Machine Learning Research. 38: 699-707.  0.88
2014 Wang J, Srebro N, Evans J. Active collaborative permutation learning Proceedings of the Acm Sigkdd International Conference On Knowledge Discovery and Data Mining. 502-511. DOI: 10.1145/2623330.2623730  0.88
2014 Shamir O, Srebro N. Distributed stochastic optimization and learning 2014 52nd Annual Allerton Conference On Communication, Control, and Computing, Allerton 2014. 850-857. DOI: 10.1109/ALLERTON.2014.7028543  0.88
2014 Shamir O, Srebro N, Zhang T. Communication-efficient distributed optimization using an approximate Newton-type method 31st International Conference On Machine Learning, Icml 2014. 3: 2665-2681.  0.88
2014 Neyshabur B, Makarychev Y, Srebro N. Clustering, Hamming embedding, Generalized LSH the max norm Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8776: 306-320.  0.88
2013 Sabato S, Srebro N, Tishby N. Distribution-dependent sample complexity of large margin learning Journal of Machine Learning Research. 14: 2119-2149.  0.88
2013 Cotter A, Srebro N, Shalev-Shwartz S. Learning optimally sparse support vector machines 30th International Conference On Machine Learning, Icml 2013. 266-274.  0.88
2013 Sabato S, Sarwate AD, Srebro N. Auditing: Active learning with outcome-dependent query costs Advances in Neural Information Processing Systems 0.88
2013 Neyshabur B, Yadollahpour P, Makarychev Y, Salakhutdinov R, Srebro N. The power of asymmetry in binary hashing Advances in Neural Information Processing Systems 0.88
2013 Takáč M, Bijral A, Richtárik P, Srebro N. Mini-batch primal and dual methods for SVMs 30th International Conference On Machine Learning, Icml 2013. 2059-2067.  0.88
2013 Arora R, Cotter A, Srebro N. Stochastic optimization of PCA with capped MSG Advances in Neural Information Processing Systems 0.88
2012 Arora R, Cotter A, Livescu K, Srebro N. Stochastic optimization for PCA and PLS 2012 50th Annual Allerton Conference On Communication, Control, and Computing, Allerton 2012. 861-868. DOI: 10.1109/Allerton.2012.6483308  0.88
2012 Foygel R, Srebro N, Salakhutdinov R. Matrix reconstruction with the local max norm Advances in Neural Information Processing Systems. 2: 936-943.  0.88
2012 Argyriou A, Foygel R, Srebro N. Sparse prediction with the κ-support norm Advances in Neural Information Processing Systems. 2: 1457-1465.  0.88
2012 Cotter A, Shalev-Shwartz S, Srebro N. The kernelized stochastic batch perceptron Proceedings of the 29th International Conference On Machine Learning, Icml 2012. 1: 943-950.  0.88
2012 Jalali A, Srebro N. Clustering using max-norm constrained optimization Proceedings of the 29th International Conference On Machine Learning, Icml 2012. 1: 481-488.  0.88
2012 Ben-David S, Loker D, Srebro N, Sridharan K. Minimizing the misclassification error rate using a surrogate convex loss Proceedings of the 29th International Conference On Machine Learning, Icml 2012. 2: 1863-1870.  0.88
2012 Peng J, Hazan T, Srebro N, Xu J. Approximate inference by intersecting semidefinite bound and local polytope Journal of Machine Learning Research. 22: 868-876.  0.88
2012 Mannor S, Srebro N. Preface - COLT2012 Journal of Machine Learning Research. 23: 1.1-1.2.  0.88
2011 Kawanishi M, Narikiyo T, Kaneko T, Srebro N. Fixed-structure H∞ controller design based on Distributed Probabilistic Model-Building Genetic Algorithm Proceedings of the Iasted International Conference On Intelligent Systems and Control. 127-132. DOI: 10.2316/P.2011.744-072  0.88
2011 Zhou X, Belkin M, Srebro N. An iterated graph laplacian approach for ranking on manifolds Proceedings of the Acm Sigkdd International Conference On Knowledge Discovery and Data Mining. 877-885. DOI: 10.1145/2020408.2020556  0.88
2011 Cotter A, Srebro N, Keshet J. A GPU-tailored approach for training kernelized SVMs Proceedings of the Acm Sigkdd International Conference On Knowledge Discovery and Data Mining. 805-813. DOI: 10.1145/2020408.2020548  0.88
2011 Shalev-Shwartz S, Singer Y, Srebro N, Cotter A. Pegasos: Primal estimated sub-gradient solver for SVM Mathematical Programming. 127: 3-30. DOI: 10.1007/s10107-010-0420-4  0.88
2011 Hazan E, Koren T, Srebro N. Beating SGD: Learning SVMs in sublinear time Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011 0.88
2011 Cotter A, Shamir O, Srebro N, Sridharan K. Better mini-batch algorithms via accelerated gradient methods Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011 0.88
2011 Foygel R, Salakhutdinov R, Shamir O, Srebro N. Learning with the weighted trace-norm under arbitrary sampling distributions Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011 0.88
2011 Zhou X, Srebro N. Error analysis of laplacian eigenmaps for semi-supervised learning Journal of Machine Learning Research. 15: 901-908.  0.88
2011 Srebro N, Sridharan K, Tewari A. On the universality of online Mirror Descent Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011 0.88
2011 Bijral AS, Ratliff N, Srebro N. Semi-supervised learning with density based distances Proceedings of the 27th Conference On Uncertainty in Artificial Intelligence, Uai 2011. 43-60.  0.88
2011 Foygel R, Srebro N. Concentration-based guarantees for low-rank matrix reconstruction Journal of Machine Learning Research. 19: 315-339.  0.88
2010 Shalev-Shwartz S, Srebro N, Zhang T. Trading accuracy for sparsity in optimization problems with sparsity constraints Siam Journal On Optimization. 20: 2807-2832. DOI: 10.1137/090759574  0.88
2010 Lee J, Recht B, Salakhutdinov R, Srebro N, Tropp JA. Practical large-scale optimization for max-norm regularization Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010 0.88
2010 Salakhutdinov R, Srebro N. Collaborative filtering in a non-uniform world: Learning with the weighted trace norm Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010 0.88
2010 Sabato S, Srebro N, Tishby N. Tight sample complexity of large-margin learning Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010 0.88
2010 Sabato S, Srebro N, Tishby N. Reducing label complexity by learning from bags Journal of Machine Learning Research. 9: 685-692.  0.88
2010 Srebro N, Sridharan K, Tewari A. Smoothness, low-noise and fast rates Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010 0.88
2010 Shalev-Shwartz S, Shamir O, Srebro N, Sridharan K. Learnability, stability and uniform convergence Journal of Machine Learning Research. 11: 2635-2670.  0.88
2009 Shalev-Shwartz S, Shamir O, Srebro N, Sridharan K. Stochastic convex optimization Colt 2009 - the 22nd Conference On Learning Theory 0.88
2009 Shalev-Shwartz S, Shamir O, Srebro N, Sridharan K. Learnability and stability in the general learning setting Colt 2009 - the 22nd Conference On Learning Theory 0.88
2009 Nadler B, Srebro N, Zhou X. Semi-supervised learning with the graph laplacian: The limit of infinite unlabelled data Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. 1331-1338.  0.88
2009 Sridharan K, Srebro N, Shalev-Shwartz S. Fast rates for regularized objectives Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 1545-1552.  0.88
2008 Balcan MF, Blum A, Srebro N. A theory of learning with similarity functions Machine Learning. 72: 89-112. DOI: 10.1007/s10994-008-5059-5  0.88
2008 Chandrasekaran V, Srebro N, Harsha P. Complexity of inference in graphical models Proceedings of the 24th Conference On Uncertainty in Artificial Intelligence, Uai 2008. 70-78.  0.88
2008 Balcan MF, Blum A, Srebro N. Improved guarantees for learning via similarity functions 21st Annual Conference On Learning Theory, Colt 2008. 287-298.  0.88
2008 Shalev-Shwartz S, Srebro N. SVM optimization: Inverse dependence on training set size Proceedings of the 25th International Conference On Machine Learning. 928-935.  0.88
2007 Amit Y, Fink M, Srebro N, Ullman S. Uncovering shared structures in multiclass classification Acm International Conference Proceeding Series. 227: 17-24. DOI: 10.1145/1273496.1273499  0.88
2007 Rosset S, Swirszcz G, Srebro N, Zhu J. ℓ Regularization in infinite dimensional feature spaces Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4539: 544-558.  0.88
2007 Srebro N. Are there local maxima in the infinite-sample likelihood of Gaussian mixture estimation? Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4539: 628-629.  0.88
2007 Srebro N. How good is a kernel when used as a similarity measure? Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4539: 323-335.  0.88
2006 Srebro N, Shakhnarovich G, Roweis S. An investigation of computational and informational limits in Gaussian mixture clustering Acm International Conference Proceeding Series. 148: 865-872. DOI: 10.1145/1143844.1143953  0.88
2006 Srebro N, Ben-David S. Learning bounds for support vector machines with learned kernels Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4005: 169-183.  0.88
2005 Rennie JDM, Srebro N. Fast Maximum Margin Matrix Factorization for collaborative prediction Icml 2005 - Proceedings of the 22nd International Conference On Machine Learning. 713-720. DOI: 10.1145/1102351.1102441  0.88
2005 Srebro N, Shraibman A. Rank, trace-norm and max-norm Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3559: 545-560.  0.88
2005 Srebro N, Alon N, Jaakkola TS. Generalization error bounds for collaborative prediction with low-rank matrices Advances in Neural Information Processing Systems 0.88
2005 Srebro N, Rennie JDM, Jaakkola TS. Maximum-margin matrix factorization Advances in Neural Information Processing Systems 0.88
2004 Thomas EE, Srebro N, Sebat J, Navin N, Healy J, Mishra B, Wigler M. Distribution of short paired duplications in mammalian genomes. Proceedings of the National Academy of Sciences of the United States of America. 101: 10349-54. PMID 15240876 DOI: 10.1073/pnas.0403727101  0.88
2004 Srebro N, Jaakkola T. Linear dependent dimensionality reduction Advances in Neural Information Processing Systems 0.88
2003 Bar-Joseph Z, Demaine ED, Gifford DK, Srebro N, Hamel AM, Jaakkola TS. K-ary clustering with optimal leaf ordering for gene expression data. Bioinformatics (Oxford, England). 19: 1070-8. PMID 12801867 DOI: 10.1093/bioinformatics/btg030  0.88
2003 Srebro N. Maximum likelihood bounded tree-width Markov networks Artificial Intelligence. 143: 123-138. DOI: 10.1016/S0004-3702(02)00360-0  0.88
2003 Srebro N, Jaakkola T. Weighted Low-Rank Approximations Proceedings, Twentieth International Conference On Machine Learning. 2: 720-727.  0.88
2001 Karger D, Srebro N. Learning Markov networks: Maximum bounded tree-width graphs Proceedings of the Annual Acm-Siam Symposium On Discrete Algorithms. 392-401.  0.88
Show low-probability matches.