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Matrix completion is the study of recovering an underlying matrix from a sparse subset of noisy observations.
High-dimensional principal component analysis with heterogeneous missingness
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Matrix completion, counterfactuals, and factor analysis of missing data
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Ma, W. and Chen, G. H. (2019) · 1910
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Sur les applications de la theorie des probabilites aux experiences agricoles: Essai des principes
Neyman, J. (1923) · 1923
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Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · 1974
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Inference and missing data
Rubin, D. B. (1976) · 1976
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Arbitrage, factor structure, and mean-variance analysis on large asset markets
Chamberlain, G. and Rothschild, M. (1983) · 1983
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Using collaborative filtering to weave an information tapestry
Goldberg, D., Nichols, D., Oki, B. M., and Terry, D. (1992) · 1992
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Bayesian pca
Bishop, C. M. (1999) · 1999
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Probabilistic principal component analysis
Tipping, M. E. and Bishop, C. M. (1999) · 1999
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The economic costs of conflict: A case study of the basque country
Abadie, A. and Gardeazabal, J. (2003) · 2003
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Consensus algorithms for the generation of all maximal bicliques
Alexe, G., Alexe, S., Crama, Y., Foldes, S., Hammer, P., and Simeone, B. (2003) · 2003
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Amazon. com recommendations: Item-to-item collaborative filtering
Linden, G., Smith, B., and York, J. (2003) · 2003
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Maximum-margin matrix factorization
Srebro, N., Rennie, J., and Jaakkola, T. (2004) · 2004
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Agarwal, A., Shah, D., and Shen, D. (2021b) · 2006
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Netflix update: Try this at home
Funk, S. (2006) · 2006
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Using mixture models for collaborative filtering
Kleinberg, J. and Sandler, M. (2008) · 2008
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Factorization meets the neighborhood: A multifaceted collaborative filtering model
Koren, Y. (2008) · 2008
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Probabilistic matrix factorization
Mnih, A. and Salakhutdinov, R. R. (2008) · 2008
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Synthetic control methods for comparative case studies: Estimating the effect of california’s tobacco control program
Abadie, A., Diamond, A., and Hainmueller, J. (2010) · 2010
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On principal component regression in a high-dimensional error-in-variables setting
Agarwal, A., Shah, D., and Shen, D. (2021a) · 2010
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Low-rank approximations of nonseparable panel models
Fernández-Val, I., Freeman, H., and Weidner, M. (2020) · 2010
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Spectral regularization algorithms for learning large incomplete matrices
Mazumder, R., Hastie, T., and Tibshirani, R. (2010) · 2010
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Collaborative filtering in a non-uniform world: Learning with the weighted trace norm
Srebro, N. and Salakhutdinov, R. R. (2010) · 2010
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A simpler approach to matrix completion
Recht, B. (2011) · 2011
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An ℓ ∞ \ell_{\infty} eigenvector perturbation bound and its application to robust covariance estimation
Fan, J., Wang, W., and Zhong, Y. (2018) · 2018
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Causal inference with noisy and missing covariates via matrix factorization
Kallus, N., Mao, X., and Udell, M. (2018) · 2018
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High-dimensional probability: An introduction with applications in data science
Vershynin, R. (2018) · 2018
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Mrsc: Multi-dimensional robust synthetic control
Amjad, M., Misra, V., Shah, D., and Shen, D. (2019) · 2019
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Synthetic difference in differences
Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., and Wager, S. (2019) · 2019
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Inference and uncertainty quantification for noisy matrix completion
Chen, Y., Fan, J., Ma, C., and Yan, Y. (2019) · 2019
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Davenport, M. A., Plan, Y., van den Berg, E., and Wootters, M. (2014) · 2014
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The optimal hard threshold for singular values is 4 / 3 4/\sqrt{3}
Gavish, M. and Donoho, D. L. (2014) · 2014
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On finding bicliques in bipartite graphs: A novel algorithm and its application to the integration of diverse biological data types
Zhang, Y., Phillips, C., Rogers, G., Baker, E., Chesler, E., and Langston, M. (2014) · 2014
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Matrix estimation by universal singular value thresholding
Chatterjee, S. (2015) · 2015
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Matrix completion and low-rank svd via fast alternating least squares
Hastie, T., Mazumder, R., Lee, J. D., and Zadeh, R. (2015) · 2015
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Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Imbens, G. W. and Rubin, D. B. (2015) · 2015
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Advances in collaborative filtering
Koren, Y. and Bell, R. (2015) · 2015
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Statistical analysis with missing data
Little, R. J. and Rubin, D. B. (2019) · 2019
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Why are big data matrices approximately low rank?
Udell, M. and Townsend, A. (2019) · 2019
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Doubly robust joint learning for recommendation on data missing not at random
Wang, X., Zhang, R., Sun, Y., and Qi, J. (2019) · 2019
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Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality
Cai, C., Poor, H. V., and Chen, Y. (2020) · 2020
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Biclique: an r package for maximal biclique enumeration in bipartite graphs
Lu, Y., Phillips, C., and Langston, M. (2020) · 2020
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Maximum biclique search at billion scale
Lyu, B., Qin, L., Lin, X., Zhang, Y., Qian, Z., and Zhou, J. (2020) · 2020
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Causal inference for recommender systems
Wang, Y., Liang, D., Charlin, L., and Blei, D. M. (2020) · 2020
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Agarwal, A. and Singh, R. (2021) · 2021
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Matrix completion methods for causal panel data models
Athey, S., Bayati, M., Doudchenko, N., Imbens, G., and Khosravi, K. (2021) · 2021
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Matrix completion with data-dependent missingness probabilities
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Nonconvex low-rank tensor completion from noisy data
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Tenips: Inverse propensity sampling for tensor completion
Yang, C., Ding, L., Wu, Z., and Udell, M. (2021) · 2021
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Matrix completion from noisy entries
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The power of convex relaxation: Near-optimal matrix completion
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