Furst, J. Jackson, M. Kearns, Y. Mansour, and S. Weakly learning DNF and characterizing statistical query learning using Fourier analysis. Kalai, and H. Noise-tolerant learning, the parity problem, and the statistical query model. Feldman, E. Grigorescu, L. Reyzin, S. Statistical algorithms and a lower bound for detecting planted cliques. Feldman and V. Computational Bounds on Statistical Query Learning.

Feldman, W.

## Forum Mathematicum

Perkins, and S. On the complexity of random satisfiability problems with planted solutions. Efficient noise-tolerant learning from statistical queries. Journal of the ACM, 45 6 —, Feldman and L. Experience-induced neural circuits that achieve high capacity. Neural computation, 21 10 , , Papadimitriou and S. Unsupervised Learning through Prediction in a Model of Cortex. A neuroidal architecture for cognitive computation. ACM, 47 5 —, Memorization and association on a realistic neural model.

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Neural Computation, 17 3 —, A quantitative theory of neural computation. Biological Cybernetics, 95 3 —, Leslie G. The hippocampus as a stable memory allocator for cortex. Neural Computation, 24 11 —, Arora, A. Bhaskara, R. Ge, and T. Provable bounds for learning some deep representations. Bengio, P. Lamblin, D. Popovici, H. Greedy layer-wise training of deep networks. Hinton, S. Osindero, and Y. A fast learning algorithm for deep belief nets. Neural Comput. Poultney, S. Chopra, and Y. Efficient learning of sparse representations with an energy-based model.

Ziv Bar-Yossef, T. Jayram, Ravi Kumar, and D. An information statistics approach to data stream and communication complexity. The space complexity of approximating the frequency moments.

Optimal approximations of the frequency moments of data streams. In STOC , pages , Mahoney, and Petros Drineas. An improved approximation algorithm for the column subset selection problem. In SODA , pages , Numerical linear algebra in the streaming model.

Frieze and R. A new approach to the planted clique problem. Alon, M. Krivelevich, and B. Finding a large hidden clique in a random graph. Random Structures and Algorithms , , The probabilistic analysis of some combinatorial search algorithms. Expected complexity of graph partitioning problems.

Discrete Applied Mathematics , , Feige and R. Finding and certifying a large hidden clique in a semirandom graph. Random Structures and Algorithms , 16 2 , Spectral partitioning of random graphs. In FOCS , pages , Large cliques elude the metropolis process. Random Structures and Algorithms , 3 4 , Spectral norm of random matrices.

The eigenvalues of random symmetric matrices. Combinatorica , 1 3 , Random vectors in the isotropic position. Journal of Functional Analysis , , Rudelson and R. Sampling from large matrices: An approach through geometric functional analysis. Journal of the ACM , 54 4 , Dempster, N. Laird, and D. Maximum likelihood from incomplete data via the em algorithm. JRSS B , , Some methods for classification and analysis of multivariate observations. Random tensors and planted cliques.

Feldman, R. Servedio, and R. Pac learning axis-aligned mixtures of gaussians with no separation assumption. The geometry of logconcave functions and sampling algorithms.

Random Structures and Algorithms , 30 3 , Matrix Perturbation Theory. Academic Press, Inc.

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## Building Bridges: Between Mathematics and Computer Science / Edition 1

A framework for robust subspace learning. International Journal on Computer Vision , , Duda, P. Hart, and D. Pattern Classification.

## Guide to Mathematics | landjasomeppe.cf

Introduction to Statistical Pattern Recognition. Berry, S.

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