papers-we-love_papers-we-love/machine_learning/README.md

Machine Learning

External Papers

• Top 10 algorithms in data mining - While it is difficult to identify the top 10, this paper contains 10 very important data mining/machine learning algorithms

• A Few Useful Things to Know about Machine Learning - Just like the title says, it contains many useful tips and gotchas for machine learning

• Random Forests - The initial paper on random forests

• Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data - The paper introducing conditional random fields as a framework for building probabilistic models.

• Support-Vector Networks - The initial paper on support-vector networks for classification.

• The Fast Johnson-Lindenstrauss Transforms

  The Johnson-Lindenstrauss transform (JLT) prescribes that there exists a matrix of size k x d, where k = O(1/eps^2 log d) such that with high probability, a matrix A drawn from this distribution preserves pairwise distances up to epsilon (e.g. (1-eps) * ||x-y|| < ||Ax - Ay|| < (1+eps) ||x-y||). This paper was the first paper to show that you can actually compute the JLT in less that O(kd) operations (e.g. you don't need to do the full matrix multiplication). They used their faster algorithm to construct one of the fastest known approximate nearest neighbor algorithms.

  Ailon, Nir, and Bernard Chazelle. "The fast Johnson-Lindenstrauss transform and approximate nearest neighbors." SIAM Journal on Computing 39.1 (2009): 302-322. Available: https://www.cs.princeton.edu/~chazelle/pubs/FJLT-sicomp09.pdf

• Applications of Machine Learning to Location Data - Using machine learning to design and analyze novel algorithms that leverage location data.

Hosted Papers

• 📜 A Sparse Johnson-Lindenstrauss Transform

  The JLT is still computationally expensive for a lot of applications and one goal would be to minimize the overall operations needed to do the aforementioned matrix multiplication. This paper showed that a goal of a O(k log d) algorithm (e.g. (log(d))^2) may be attainable by showing that very sparse, structured random matrices could provide the JL guarantee on pairwise distances.

  Dasgupta, Anirban, Ravi Kumar, and Tamás Sarlós. "A sparse johnson: Lindenstrauss transform." Proceedings of the forty-second ACM symposium on Theory of computing. ACM, 2010. Available: arXiv/cs/1004:4240

• 📜 Towards a unified theory of sparse dimensionality reduction in Euclidean space

  This paper attempts to layout the generic mathematical framework (in terms of convex analysis and functional analysis) for sparse dimensionality reduction. The first author is a Fields Medalist who is interested in taking techniques for Banach Spaces and applying them to this problem. This paper is a very technical paper that attempts to answer the question, "when does a sparse embedding exist deterministically?" (e.g. doesn't require drawing random matrices).

  Bourgain, Jean, and Jelani Nelson. "Toward a unified theory of sparse dimensionality reduction in euclidean space." arXiv preprint arXiv:1311.2542; Accepted in an AMS Journal but unpublished at the moment (2013). Available: http://arxiv.org/abs/1311.2542