Year |
Citation |
Score |
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 |
0.569 |
|
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 |
0.498 |
|
2013 |
Sabato S, Srebro N, Tishby N. Distribution-dependent sample complexity of large margin learning Journal of Machine Learning Research. 14: 2119-2149. |
0.592 |
|
2013 |
Cotter A, Srebro N, Shalev-Shwartz S. Learning optimally sparse support vector machines 30th International Conference On Machine Learning, Icml 2013. 266-274. |
0.418 |
|
2013 |
Sabato S, Sarwate AD, Srebro N. Auditing: Active learning with outcome-dependent query costs Advances in Neural Information Processing Systems. |
0.396 |
|
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. |
0.56 |
|
2011 |
Zhou X, Srebro N. Error analysis of laplacian eigenmaps for semi-supervised learning Journal of Machine Learning Research. 15: 901-908. |
0.474 |
|
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. |
0.576 |
|
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. |
0.516 |
|
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. |
0.379 |
|
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. |
0.601 |
|
2010 |
Sabato S, Srebro N, Tishby N. Reducing label complexity by learning from bags Journal of Machine Learning Research. 9: 685-692. |
0.575 |
|
2010 |
Liang P, Srebro N. On the interaction between norm and dimensionality: Multiple regimes in learning Icml 2010 - Proceedings, 27th International Conference On Machine Learning. 647-654. |
0.521 |
|
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. |
0.504 |
|
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. |
0.451 |
|
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 |
0.329 |
|
2008 |
Balcan MF, Blum A, Srebro N. Improved guarantees for learning via similarity functions 21st Annual Conference On Learning Theory, Colt 2008. 287-298. |
0.548 |
|
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. |
0.56 |
|
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. |
0.371 |
|
Low-probability matches (unlikely to be authored by this person) |
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. |
0.28 |
|
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 |
0.244 |
|
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 |
0.189 |
|
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. |
0.157 |
|
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 |
0.153 |
|
2017 |
Bijral AS, Sarwate AD, Srebro N. Data-Dependent Convergence for Consensus Stochastic Optimization Ieee Transactions On Automatic Control. 62: 4483-4498. DOI: 10.1109/Tac.2017.2671377 |
0.127 |
|
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. |
0.102 |
|
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. |
0.095 |
|
2003 |
Srebro N. Maximum likelihood bounded tree-width Markov networks Artificial Intelligence. 143: 123-138. DOI: 10.1016/S0004-3702(02)00360-0 |
0.083 |
|
2017 |
Wang J, Lee JD, Mahdavi M, Kolar M, Srebro N. Sketching meets random projection in the dual: A provable recovery algorithm for big and high-dimensional data Electronic Journal of Statistics. 11: 4896-4944. DOI: 10.1214/17-Ejs1334Si |
0.062 |
|
2005 |
Srebro N, Alon N, Jaakkola TS. Generalization error bounds for collaborative prediction with low-rank matrices Advances in Neural Information Processing Systems. |
0.061 |
|
2004 |
Srebro N, Jaakkola T. Linear dependent dimensionality reduction Advances in Neural Information Processing Systems. |
0.061 |
|
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. |
0.058 |
|
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 |
0.054 |
|
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. |
0.054 |
|
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. |
0.051 |
|
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 |
0.045 |
|
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 |
0.044 |
|
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 |
0.041 |
|
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. |
0.038 |
|
2010 |
Shalev-Shwartz S, Shamir O, Srebro N, Sridharan K. Learnability, stability and uniform convergence Journal of Machine Learning Research. 11: 2635-2670. |
0.036 |
|
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. |
0.035 |
|
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. |
0.032 |
|
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 |
0.031 |
|
2003 |
Srebro N, Jaakkola T. Weighted Low-Rank Approximations Proceedings, Twentieth International Conference On Machine Learning. 2: 720-727. |
0.03 |
|
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 |
0.026 |
|
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 |
0.026 |
|
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. |
0.026 |
|
2011 |
Foygel R, Srebro N. Concentration-based guarantees for low-rank matrix reconstruction Journal of Machine Learning Research. 19: 315-339. |
0.025 |
|
2013 |
Arora R, Cotter A, Srebro N. Stochastic optimization of PCA with capped MSG Advances in Neural Information Processing Systems. |
0.025 |
|
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. |
0.025 |
|
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 |
0.021 |
|
2009 |
Shalev-Shwartz S, Shamir O, Srebro N, Sridharan K. Stochastic convex optimization Colt 2009 - the 22nd Conference On Learning Theory. |
0.021 |
|
2012 |
Argyriou A, Foygel R, Srebro N. Sparse prediction with the κ-support norm Advances in Neural Information Processing Systems. 2: 1457-1465. |
0.018 |
|
2015 |
Meshi O, Srebro N, Hazan T. Efficient training of structured SVMs via soft constraints Journal of Machine Learning Research. 38: 699-707. |
0.017 |
|
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. |
0.016 |
|
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. |
0.014 |
|
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. |
0.014 |
|
2013 |
Neyshabur B, Yadollahpour P, Makarychev Y, Salakhutdinov R, Srebro N. The power of asymmetry in binary hashing Advances in Neural Information Processing Systems. |
0.012 |
|
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. |
0.01 |
|
2012 |
Mannor S, Srebro N. Preface - COLT2012 Journal of Machine Learning Research. 23: 1.1-1.2. |
0.01 |
|
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. |
0.01 |
|
2012 |
Foygel R, Srebro N, Salakhutdinov R. Matrix reconstruction with the local max norm Advances in Neural Information Processing Systems. 2: 936-943. |
0.01 |
|
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. |
0.01 |
|
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 |
0.01 |
|
2005 |
Srebro N, Rennie JDM, Jaakkola TS. Maximum-margin matrix factorization Advances in Neural Information Processing Systems. |
0.01 |
|
Hide low-probability matches. |