Discussion

Nonnegative Matrix Factorization

Discussion: When does well-posedness lead to better algorithms?

Balcan, M., A. Blum, et al. “Clustering under Approximation Stability.” (PDF) Journal of the ACM (2013).

Tensor Decompositions

Discussion: When do algorithms rely (too much) on a distributional model?

Feige, U., and J. Kilian. “ Heuristics for Semirandom Graph Problems .” Journal of Computing and System Sciences 63, no. 4 (2001): 639–71.

Sparse Coding

Discussion: When does belief propagation (provably) work?

Geman, S., and D. Geman. “ Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images .” Pattern Analysis and Machine Intelligence (1984).

Learning Mixture Models

Discussion: Is nature an adversary? And if not, how can we model and exploit that?

Bhaskara, A., M. Charikar, et al. “ Smoothed Analysis of Tensor Decompositions .” Symposium on Theory of Computing (2014).

Linear Inverse Problems

Discussion: Do we have enough average-case assumptions?

Berthet, Q., and P. Rigollet. “ Computational Lower Bounds for Sparse PCA .” Conference on Learning Theory (2013).

Chandrasekaran, V., and M. Jordan. “ Computational and Statistical Tradeoffs via Convex Relaxation .” Proceedings of the National Academy of Sciences of the United States of America 110, no. 13 (2013): E1181–90.

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