views: 686

# Nathan Srebro

Professor

Sign in to view more

Show Academic Trajectory

Ego Network

D-Core

Research Interests

Author Statistics

Experience

Sign in to view more

Education

Sign in to view more

Bio

Dr. Srebro is interested in statistical and computational aspects of machine learning, and the interaction between them. He has done theoretical work in statistical learning theory and in algorithms, devised novel learning models and optimization techniques, and has worked on applications in computational biology, text analysis and collaborative filtering. Before coming to TTIC, Dr. Srebro was a postdoctoral fellow at the University of Toronto and a visiting scientist at IBM Research.

## Papers169 papers

Sort

By YearBy Citation

ICLR, (2020)

international conference on machine learning, (2019)

Bibtex

Journal of Machine Learning Research, no. 167 (2019): 1-46

conference on learning theory, (2019): 2512-2530

COLT, (2019): 1319-1345

arXiv: Learning, (2019)

ALT, (2019): 856-881

International Conference on Machine Learning, (2019): 4683-4692

COLT, (2019): 2667-2690

FAT, (2019)

CoRR, (2019)

CoRR, (2019)

Social Science Computer Review, (2019): 089443931984837

ALT, (2018)

arXiv: Optimization and Control, (2018)

arXiv: Learning, (2018)

NeurIPS, (2018)

NeurIPS, (2018): 6531-6540

international conference on learning representations, (2018)

Andrew Cotter,Maya R. Gupta, Heinrich Jiang,Nathan Srebro,Karthik Sridharan, Serena Wang, Blake E. Woodworth,Seungil You

international conference on machine learning, (2018)

arXiv: Machine Learning, (2018)

NeurIPS, (2018): 9461-9471

ICML, (2018): 1827-1836

NeurIPS, (2018): 8376-8387

international conference on artificial intelligence and statistics, (2018)

international conference on learning representations, (2018)

Bibtex

arXiv: Machine Learning, (2018)

AISTATS, (2017): 1150-1158

IEEE Trans. Automat. Contr., no. 9 (2017): 4483-4498

ICML, (2017): 3636-3645

arXiv: Optimization and Control, (2017)

NIPS, (2017): 4148-4158

NIPS, (2017): 5947-5956

COLT, (2017)

ICML, (2017): 1203-1212

arXiv: Learning, (2017)

COLT, (2017)

information theory and applications, (2017)

arXiv: Learning, (2017)

arXiv: Learning, (2016)

Allerton, pp.869-874, (2016)

arXiv: Learning, (2016)

NIPS, (2016): 3873-3881

arXiv: Learning, (2016)

NIPS, pp.4925-4933, (2016)

arXiv: Optimization and Control, (2016)

Bibtex

international conference on learning representations, (2016)

international conference on artificial intelligence and statistics, (2016)

NIPS, (2016): 3315-3323

NIPS, (2016): 3477-3485

AISTATS, pp.751-760, (2016)

Mathematical Programming, no. 1-2 (2016): 549-573

CoRR, (2015)

CoRR, (2015)

Annual Conference on Neural Information Processing Systems, (2015)

COLT, (2015): 1376-1401

Journal of Machine Learning Research, (2015)

Allerton, (2015): 688-695

(2014)

Bibtex

ALT, (2014): 306-320

NIPS, pp.1017-1025, (2014)

ICML, (2014): 1000-1008

KDD, pp.502-511, (2014)

neural information processing systems, (2013): 1815-1823

Journal of Machine Learning Research, no. Issue-in-Progress (2013): 2119-2149

neural information processing systems, (2013): 2823-2831

CoRR, (2013)

ICML, pp.266-274, (2013)

neural information processing systems, (2013): 512-520

ICML, (2013): 1022-1030

AISTATS, pp.868-876, (2012)

(2012)

Bibtex

CoRR, (2012)

ICML, (2012): 1579-1586

NIPS, pp.944-952, (2012)

(2012)

Bibtex

NIPS, (2012): 944-952

ICML, (2012)

CoRR, (2012)

NIPS, pp.1466-1474, (2012)

ICML, (2012)

AISTATS, pp.901-908, (2011)

(2011)

Cited by

**1**BibtexCoRR, (2011)

NIPS, (2011): 2133-2141

Clinical Orthopaedics and Related Research, (2011): 315-340

KDD, pp.805-813, (2011)

View All