[AV06] Rosa I Arriaga and Santosh Vempala. An algorithmic theory of learning: Robust concepts and random projection. Machine learning, 63:161-182, 2006.
[BBL06] Maria-Florina Balcan, Alina Beygelzimer, and John Langford. Agnostic active learning. In Proceedings of the 23rd international conference on Machine learning, pages 65–72, 2006.
$\left[\mathrm{BKS}^{+}06\right]$  Gilles Blanchard, Motoaki Kawanabe, Masashi Sugiyama, Vladimir Spokoiny, Klaus-Robert Müller, and Sam Roweis. In search of non-gaussian components of a high-dimensional distribution. Journal of Machine Learning Research, 7(2), 2006.
[Blu90] Avrim Blum. Learning boolean functions in an infinite attribute space. In Proceedings of the twenty-second annual ACM symposium on Theory of computing, pages 64-72, 1990.
[Blu94] Avrim Blum. Relevant examples and relevant features: Thoughts from computational learning theory. In AAAI Fall Symposium on Relevance, volume 5, page 1, 1994.
[BV08] S Charles Brubaker and Santosh S Vempala. Isotropic pca and affine-invariant clustering. Building Bridges: Between Mathematics and Computer Science, pages 241-281, 2008.
[Car98] J-F Cardoso. Multidimensional independent component analysis. In Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP'98 (Cat. No. 98CH36181), volume 4, pages 1941-1944. IEEE, 1998.
[Com94] Pierre Comon. Independent component analysis, a new concept? Signal processing, 36(3):287-314, 1994.
$\left[\mathrm{CST}^{+}00\right]$  Nello Cristianini, John Shawe-Taylor, et al. An introduction to support vector machines and other kernel-based learning methods. Cambridge university press, 2000.
[DK70] Chandler Davis and William Morton Kahan. The rotation of eigenvectors by a perturbation. iii. SIAM Journal on Numerical Analysis, 7(1):1-46, 1970.
[GVX14] Navin Goyal, Santosh Vempala, and Ying Xiao. Fourier pca and robust tensor decomposition. In Proceedings of the forty-sixth annual ACM symposium on Theory of computing, pages 584-593, 2014.
$\left[\mathrm{HDO}^{+}98\right]$  Marti A. Hearst, Susan T Dumais, Edgar Osuna, John Platt, and Bernhard Scholkopf. Support vector machines. IEEE Intelligent Systems and their applications, 13(4):18-28, 1998.
[JKV23] He Jia, Pravesh K Kothari, and Santosh S Vempala. Beyond moments: Robustly learning affine transformations with asymptotically optimal error. arXiv preprint arXiv:2302.12289, 2023.
[KKMS08] Adam Tauman Kalai, Adam R Klivans, Yishay Mansour, and Rocco A Servedio. Agnostically learning halfspaces. SIAM Journal on Computing, 37(6):1777-1805, 2008.
[LS11] Phil Long and Rocco Servedio. Algorithms and hardness results for parallel large margin learning. Advances in Neural Information Processing Systems, 24, 2011.
[LV07] László Lovász and Santosh Vempala. The geometry of logconcave functions and sampling algorithms. Random Structures & Algorithms, 30(3):307-358, 2007.
[MP69] Marvin Minsky and Seymour Papert. Perceptrons: An Introduction to Computational Geometry. MIT Press, Cambridge, MA, USA, 1969.
[PCST99] John Platt, Nello Cristianini, and John Shawe-Taylor. Large margin dags for multiclass classification. Advances in neural information processing systems, 12, 1999.
[Ros58] Frank Rosenblatt. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6):386, 1958.