Elo ratings applied to TopCoder SRM

We present an Elo-based rating method for ranked multi-player games. We justify a definition of performance using the logarithm of a player's rank. We customize the method for rating TopCoder SRM. We choose parameters which maximize the rating's prediction accuracy when applied to past SRM, and preserve similarities with existing SRM ratings. We evaluate the accuracy of the proposed system on the available data. Results show that the proposed system has a higher predictive accuracy than the existing SRM rating system, and hence could be a good alternative.

Efficient Exact Inference in Planar Ising Models

We give polynomial-time algorithms for the exact computation of lowest-energy (ground) states, worst margin violators, log partition functions, and marginal edge probabilities in certain binary undirected graphical models. Our approach provides an interesting alternative to the well-known graph cut paradigm in that it does not impose any submodularity constraints; instead we require planarity to establish a correspondence with perfect matchings (dimer coverings) in an expanded dual graph. We implement a unified framework while delegating complex but well-understood subproblems (planar embedding, maximum-weight perfect matching) to established algorithms for which efficient implementations are freely available. Unlike graph cut methods, we can perform penalized maximum-likelihood as well as maximum-margin parameter estimation in the associated conditional random fields (CRFs), and employ marginal posterior probabilities as well as maximum a posteriori (MAP) states for prediction. Maximum-margin CRF parameter estimation on image denoising and segmentation problems shows our approach to be efficient and effective. A C++ implementation is available from http://nic.schraudolph.org/isinf/