Selected bibliography

• D. Achlioptas and F. McSherry. On spectral learning of mixtures of distributions. In COLT, 2005.
• A. Anandkumar, K. Chaudhuri, D. Hsu, S.Kakade, L. Song and T. Zhang, Spectral Methods for Learning Multivariate Latent Tree Structure. Advances in Neural Information Processing Systems (NIPS) 25 , 2011.
• A. Anandkumar, D.P. Foster, D. Hsu, S.M. Kakade, and Y.K. Liu. A spectral algorithm for latent Dirichlet allocation, Advances in Neural Information Processing Systems (NIPS) 25, 2012.
• A. Anandkumar, R. Ge, D. Hsu, S. M. Kakade, and T. Telgarsky. Tensor decompositions for learning latent variable models, 2012. arXiv:1210.7559.
• A. Anandkumar, D. Hsu, and S.M. Kakade. A method of moments for mixture models and hidden Markov models. COLT, 2012.
• R. Bailly, F. Denis, L. Ralaivola. Grammatical inference as a principal component analysis problem. 26th International Conference on Machine Learning (ICML), 2009.
• R. Bailly, A. Habrard, F. Denis. A Spectral Approach for Probabilistic Grammatical Inference on Trees. ALT, 2010.
• R. Bailly. QWA: Spectral Algorithm. Journal of Machine Learning Research - Proceedings Track 20: 147-163, 2011.
• B. Balle and M. Mohri. Spectral Learning of General Weighted Automata via Constrained Matrix Completion. Neural Information Processing Systems Conference (NIPS), 2012.
• B. Balle, A. Quattoni, and X. Carreras. Local loss optimization in operator models: A new insight into spectral learning. International Conference on Machine Learning (ICML), 2012.
• B. Balle, A. Quattoni, and X. Carreras. A spectral learning algorithm for finite state transducers. European Conference on Machine Learning and Knowledge Discovery in Databases, 2011
• B. Boots and G. Gordon. Two-Manifold Problems with Applications to Nonlinear System Identification. 27th Internal Conference on Machine Learning, (ICML), 2012.
• B. Boots and G. Gordon. An Online Spectral Learning Algorithm for Partially Observable Nonlinear Dynamical Systems. The 25th Conference on Arti.cial Intelligence (AAAI), 2011.
• B. Boots and G. Gordon. Predictive State Temporal Difference Learning. Advances in Neural Information Processing Systems 24 (NIPS), 2010.
• B. Boots, S. Siddiqi and G. Gordon. Closing the Learning-Planning Loop with Predictive State Representations. Robotics: Science and Systems VI (R:SS), 2010.
• N. Bshouty and P. Long. Finding planted partitions in nearly linear time using arrested spectral clustering. In ICML, 2010.
• K. Chaudhuri, S. Kakade, K. Livescu and K. Sridharan. Multiview Clustering via Canonical Correlation Analysis. In ICML, 2009.
• K. Chaudhuri, F. Chung, and A. Tsiatas. Spectral Clustering of Graphs with General Degrees in the Extended Planted Partition Model. In COLT, 2012.
• S. Cohen and M. Collins. Tensor Decomposition for Fast Parsing with Latent-Variable PCFGs. Neural Information Processing Systems Conference (NIPS), 2012.
• S.B. Cohen, K. Stratos, M. Collins, D.P. Foster, and L. Ungar. Spectral learning of latent variable PCFGs. In Association of Computational Linguistics (ACL), volume 50, 2012.
• F. Denis, Y. Esposito, and A. Habrard. Learning Rational Stochastic Languages. Conference on Learning Theory (COLT), 2006
• P. Dhillon, D. Foster, and L. Ungar. Multi-View Learning of Word Embeddings via CCA. In NIPS, 2011.
• P. Dhillon, J. Rodu, D. Foster and L. Ungar. Two Step CCA: A new spectral method for estimating vector models of words. 29th Internal Conference on Machine Learning, (ICML), 2012.
• P. Dhillon, J. Rodu, M. Collins, D. Foster, and L. Ungar. Spectral dependency parsing with latent variables. Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing (EMNLP) and Computational Natural Language Learning (CONLL), 2012.
• P. Drineas, A. Frieze, R. Kannan, S. Vempala and V. Vinay. Clustering large graphs via the singular value decomposition. Machine Learning, 56, 9-33, 2004.
• R. Fisher, R. Simmons, C.-S. Chung, R. Cooper, G. Grindle, A. Kelleher, H. Liu, and Y.K. Wu. Spectral Machine Learning for Predicting Power Wheelchair Exercise Compliance. 21st International Symposium on Methodologies for Intelligent Systems, 2014.
• D. Hsu, S. Kakade, and T. Zhang. A spectral algorithm for learning hidden Markov models. In Proceedings of the Annual Conference on Computational Learning Theory (COLT), 2009.
• M. Ishteva, L. Song, H. Park. Unfolding Latent Tree Structures using 4th Order Tensors. ICML 2013.
• R. Kannan, H. Salamasian, and S. Vempala. The spectral method for general mixture models. In COLT, 2005.
• F.M. Luque, A. Quattoni, B. Balle, and X. Carreras. Spectral learning in non-deterministic dependency parsing. Proceedings of the 13th Conference of the European Chapter of the Association for computational Linguistics (EACL), 2012.
• M. W. Mahoney. Randomized Algorithms for Matrices and Data. Foundations and Trends in Machine Learning 3(2): 123-224 (2011).
• F. McSherry. Spectral partitioning of random graphs. In FOCS, 2001.
• E. Mossel and S. Roch. Learning nonsingular phylogenies and hidden Markov models. Annals of Applied Probability,16(2):583614, 2006.
• A.P. Parikh, L. Song and E. Xing, A spectral algorithm for latent tree graphical models. 28th International Conference on Machine Learning (ICML), 2011.
• A.P. Parikh, L. Song, M. Ishteva, G. Teodoru, and E.P. Xing. A spectral algorithm for latent junction trees. Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI), 2012.
• S. Siddiqi, B. Boots and G. Gordon, Reduced Rank Hidden Markov Models. Artificial Intelligence and Statistics (AISTATS), 2010.
• S. Siddiqi, B. Boots and G. Gordon, A Constraint Generation Approach to Learning Stable Linear Dynamical Systems. Advances in Neural Information Processing Systems 21 (NIPS), 2007.
• A. Smola, A. Gretton, L. Song, and B. Scholkopf. A Hilbert space embedding for distributions. In Algorithmic Learning Theory, 2007.
• L. Song, M. Ishteva, H. Park, A. Parikh and E. Xing. Hierarchical Tensor Decomposition of Latent Tree Graphical Models. ICML 2013.
• L. Song, A. Parikh and E. Xing. Kernel Embedding of Latent Tree Graphical Models, NIPS 2011.
• L. Song, A. Gretton, D. Bickson, Y. Low and C. Guestrin, Kernel belief propagation. Artificial Intelligence and Statistics (AISTATS), 2011.
• L. Song, B. Boots, S. Siddiqi, G. Gordon and A. Smola, Hilbert space embedding of hidden Markov model. 27th International Conference on Machine Learning, (ICML), 2010.
• S. Terwijn. On the learnability of hidden Markov models. Grammatical Inference: Algorithms and Applications, 2002.
• S. Vempala and G. Wang. A spectral algorithm for learning mixtures of distributions. In FOCS, 2002.
• J. Zou, D. Hsu, D. Parkes, and R. Adams. Contrastive learning using spectral methods. Advances in Neural Information Processing Systems (NIPS) 26, 2013.