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ORCIDMajid Janzamin
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2020 – today
- 2020
[i7]Majid Janzamin, Rong Ge, Jean Kossaifi, Anima Anandkumar:
Spectral Learning on Matrices and Tensors. CoRR abs/2004.07984 (2020)
2010 – 2019
- 2019
- [j4]Majid Janzamin, Rong Ge, Jean Kossaifi, Anima Anandkumar:
Spectral Learning on Matrices and Tensors. Found. Trends Mach. Learn. 12(5-6): 393-536 (2019) - 2017
- [j3]Animashree Anandkumar, Rong Ge, Majid Janzamin:
Analyzing Tensor Power Method Dynamics in Overcomplete Regime. J. Mach. Learn. Res. 18: 22:1-22:40 (2017) - 2016
- [b1]Majid Janzamin:
Non-convex Optimization in Machine Learning: Provable Guarantees Using Tensor Methods. University of California, Irvine, USA, 2016 - [c7]Hanie Sedghi, Majid Janzamin, Anima Anandkumar:
Provable Tensor Methods for Learning Mixtures of Generalized Linear Models. AISTATS 2016: 1223-1231 - 2015
- [j2]Animashree Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade:
When are overcomplete topic models identifiable? uniqueness of tensor tucker decompositions with structured sparsity. J. Mach. Learn. Res. 16: 2643-2694 (2015) - [c6]Animashree Anandkumar, Rong Ge, Majid Janzamin:
Learning Overcomplete Latent Variable Models through Tensor Methods. COLT 2015: 36-112 - [c5]Majid Janzamin, Hanie Sedghi, U. N. Niranjan, Animashree Anandkumar:
FEAST at Play: Feature ExtrAction using Score function Tensors. FE@NIPS 2015: 130-144 - [c4]Majid Janzamin, Hanie Sedghi, Anima Anandkumar:
Score Function Features for Discriminative Learning. ICLR (Workshop) 2015 - [i6]Majid Janzamin, Hanie Sedghi, Anima Anandkumar:
Generalization Bounds for Neural Networks through Tensor Factorization. CoRR abs/1506.08473 (2015) - 2014
- [j1]Majid Janzamin, Animashree Anandkumar:
High-dimensional covariance decomposition into sparse Markov and independence models. J. Mach. Learn. Res. 15(1): 1549-1591 (2014) - [i5]Animashree Anandkumar, Rong Ge, Majid Janzamin:
Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank-1 Updates. CoRR abs/1402.5180 (2014) - [i4]Animashree Anandkumar, Rong Ge, Majid Janzamin:
Provable Learning of Overcomplete Latent Variable Models: Semi-supervised and Unsupervised Settings. CoRR abs/1408.0553 (2014) - [i3]Anima Anandkumar, Rong Ge, Majid Janzamin:
Analyzing Tensor Power Method Dynamics: Applications to Learning Overcomplete Latent Variable Models. CoRR abs/1411.1488 (2014) - [i2]Majid Janzamin, Hanie Sedghi, Anima Anandkumar:
Score Function Features for Discriminative Learning: Matrix and Tensor Framework. CoRR abs/1412.2863 (2014) - 2013
- [c3]Anima Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade:
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity. NIPS 2013: 1986-1994 - [i1]Animashree Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade:
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity. CoRR abs/1308.2853 (2013) - 2012
- [c2]Majid Janzamin, Animashree Anandkumar:
High-Dimensional Covariance Decomposition into Sparse Markov and Independence Domains. ICML 2012 - 2010
- [c1]Majid Janzamin, Mohammad Reza Pakravan, Hanie Sedghi:
A Game-Theoretic Approach for Power Allocation in Bidirectional Cooperative Communication. WCNC 2010: 1-6
Coauthor Index
aka: Animashree Anandkumar
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