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[1] Arnak S. Dalalyan,et al. Outlier-robust estimation of a sparse linear model using 𝓁1-penalized Huber's M-estimator , 2019, NeurIPS.
[2] Massimiliano Pontil,et al. Excess risk bounds for multitask learning with trace norm regularization , 2012, COLT.
[3] Sujay Sanghavi,et al. Iterative Least Trimmed Squares for Mixed Linear Regression , 2019, NeurIPS.
[4] Liu Liu,et al. High Dimensional Robust Sparse Regression , 2018, AISTATS.
[5] Roman Vershynin,et al. Introduction to the non-asymptotic analysis of random matrices , 2010, Compressed Sensing.
[6] Shie Mannor,et al. Outlier-Robust PCA: The High-Dimensional Case , 2013, IEEE Transactions on Information Theory.
[7] Jerry Li,et al. Being Robust (in High Dimensions) Can Be Practical , 2017, ICML.
[8] Pravesh Kothari,et al. Efficient Algorithms for Outlier-Robust Regression , 2018, COLT.
[9] Bradley P. Carlin,et al. BAYES AND EMPIRICAL BAYES METHODS FOR DATA ANALYSIS , 1996, Stat. Comput..
[10] Inderjit S. Dhillon,et al. Mixed Linear Regression with Multiple Components , 2016, NIPS.
[11] Prasad Raghavendra,et al. List Decodable Learning via Sum of Squares , 2019, SODA.
[12] Hugo Larochelle,et al. Optimization as a Model for Few-Shot Learning , 2016, ICLR.
[13] Pravesh Kothari,et al. Robust moment estimation and improved clustering via sum of squares , 2018, STOC.
[14] Sidhanth Mohanty,et al. List Decodable Mean Estimation in Nearly Linear Time , 2020, 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS).
[15] Percy Liang,et al. Spectral Experts for Estimating Mixtures of Linear Regressions , 2013, ICML.
[16] Yuanzhi Li,et al. Learning Mixtures of Linear Regressions with Nearly Optimal Complexity , 2018, COLT.
[17] Daniel M. Kane,et al. List-decodable robust mean estimation and learning mixtures of spherical gaussians , 2017, STOC.
[18] Constantine Caramanis,et al. Solving a Mixture of Many Random Linear Equations by Tensor Decomposition and Alternating Minimization , 2016, ArXiv.
[19] Aravindan Vijayaraghavan,et al. On Learning Mixtures of Well-Separated Gaussians , 2017, 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS).
[20] Alexandre Lacoste,et al. TADAM: Task dependent adaptive metric for improved few-shot learning , 2018, NeurIPS.
[21] Emmanuel J. Candès,et al. Tight Oracle Inequalities for Low-Rank Matrix Recovery From a Minimal Number of Noisy Random Measurements , 2011, IEEE Transactions on Information Theory.
[22] J. Steinhardt. Lecture Notes for STAT260 (Robust Statistics) , 2019 .
[23] Hugo Larochelle,et al. Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples , 2019, ICLR.
[24] Shahar Mendelson,et al. Mean Estimation and Regression Under Heavy-Tailed Distributions: A Survey , 2019, Found. Comput. Math..
[25] Prateek Jain,et al. Consistent Robust Regression , 2017, NIPS.
[26] Prateek Jain,et al. Robust Regression via Hard Thresholding , 2015, NIPS.
[27] Gregory Valiant,et al. Estimating Learnability in the Sublinear Data Regime , 2018, NeurIPS.
[28] Pravesh Kothari,et al. List-Decodable Subspace Recovery via Sum-of-Squares , 2020, ArXiv.
[29] C A Nelson,et al. Learning to Learn , 2017, Encyclopedia of Machine Learning and Data Mining.
[30] Massimiliano Pontil,et al. Convex multi-task feature learning , 2008, Machine Learning.
[31] Ilias Diakonikolas,et al. Robustly Learning any Clusterable Mixture of Gaussians , 2020, ArXiv.
[32] Prasad Raghavendra,et al. High-dimensional estimation via sum-of-squares proofs , 2018, Proceedings of the International Congress of Mathematicians (ICM 2018).
[33] Shimon Ullman,et al. Uncovering shared structures in multiclass classification , 2007, ICML '07.
[34] Weihao Kong,et al. Sublinear Optimal Policy Value Estimation in Contextual Bandits , 2019, AISTATS.
[35] Pravesh Kothari,et al. Better Agnostic Clustering Via Relaxed Tensor Norms , 2017, ArXiv.
[36] Anima Anandkumar,et al. Provable Tensor Methods for Learning Mixtures of Generalized Linear Models , 2014, AISTATS.
[37] Michael I. Jordan,et al. Provable Meta-Learning of Linear Representations , 2020, ICML.
[38] Gregory Valiant,et al. Learning from untrusted data , 2016, STOC.
[39] Adam R. Klivans,et al. List-Decodable Linear Regression , 2019, NeurIPS.
[40] Shuicheng Yan,et al. Robust PCA in High-dimension: A Deterministic Approach , 2012, ICML.
[41] Santosh S. Vempala,et al. A spectral algorithm for learning mixture models , 2004, J. Comput. Syst. Sci..
[42] Prateek Jain,et al. Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems , 2019, AISTATS.
[43] He Jia,et al. Robustly Clustering a Mixture of Gaussians , 2019, ArXiv.
[44] Prateek Jain,et al. Thresholding based Efficient Outlier Robust PCA , 2017, ArXiv.
[45] Jerry Li,et al. Mixture models, robustness, and sum of squares proofs , 2017, STOC.
[46] Razvan Pascanu,et al. Meta-Learning with Latent Embedding Optimization , 2018, ICLR.
[47] Samuel B. Hopkins. Mean estimation with sub-Gaussian rates in polynomial time , 2018, The Annals of Statistics.
[48] Geoffrey J. Gordon,et al. Closed-form supervised dimensionality reduction with generalized linear models , 2008, ICML '08.
[49] Thomas L. Griffiths,et al. Recasting Gradient-Based Meta-Learning as Hierarchical Bayes , 2018, ICLR.
[50] Jerry Li,et al. Sever: A Robust Meta-Algorithm for Stochastic Optimization , 2018, ICML.
[51] Gregory Valiant,et al. Resilience: A Criterion for Learning in the Presence of Arbitrary Outliers , 2017, ITCS.
[52] Alon Orlitsky,et al. Supervised dimensionality reduction using mixture models , 2005, ICML.
[53] Tong Zhang,et al. A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data , 2005, J. Mach. Learn. Res..
[54] Santosh S. Vempala,et al. Efficient Representations for Lifelong Learning and Autoencoding , 2014, COLT.
[55] Ilias Diakonikolas,et al. Efficient Algorithms and Lower Bounds for Robust Linear Regression , 2018, SODA.
[56] Yu Cheng,et al. High-Dimensional Robust Mean Estimation in Nearly-Linear Time , 2018, SODA.
[57] Yuanzhi Li,et al. Even Faster SVD Decomposition Yet Without Agonizing Pain , 2016, NIPS.
[58] Peter L. Bartlett,et al. Fast Mean Estimation with Sub-Gaussian Rates , 2019, COLT.
[59] Sham M. Kakade,et al. Few-Shot Learning via Learning the Representation, Provably , 2020, ICLR.
[60] Matthijs Douze,et al. Large-scale image classification with trace-norm regularization , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[61] Jerry Li,et al. Computationally Efficient Robust Sparse Estimation in High Dimensions , 2017, COLT.
[62] Sivaraman Balakrishnan,et al. Robust estimation via robust gradient estimation , 2018, Journal of the Royal Statistical Society: Series B (Statistical Methodology).
[63] Adam Tauman Kalai,et al. Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning , 2019, ALT.
[64] Joel A. Tropp,et al. An Introduction to Matrix Concentration Inequalities , 2015, Found. Trends Mach. Learn..
[65] G. Lugosi,et al. Robust multivariate mean estimation: The optimality of trimmed mean , 2019, The Annals of Statistics.
[66] Gregory R. Koch,et al. Siamese Neural Networks for One-Shot Image Recognition , 2015 .
[67] Chao Gao. Robust regression via mutivariate regression depth , 2017, Bernoulli.
[68] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[69] Martial Hebert,et al. Learning to Model the Tail , 2017, NIPS.
[70] E. Ordentlich,et al. Inequalities for the L1 Deviation of the Empirical Distribution , 2003 .
[71] Huan Xu,et al. A Unified Framework for Outlier-Robust PCA-like Algorithms , 2015, ICML.
[72] Eric Price,et al. Compressed Sensing with Adversarial Sparse Noise via L1 Regression , 2018, SOSA.
[73] Jonathan Baxter,et al. A Model of Inductive Bias Learning , 2000, J. Artif. Intell. Res..
[74] Zhao Song,et al. Learning mixtures of linear regressions in subexponential time via Fourier moments , 2019, STOC.
[75] Weihao Kong,et al. Meta-learning for mixed linear regression , 2020, ICML.