In this paper we propose a class of efficient Generalized Method-of-Moments (GMM) algorithms for computing parameters of the Plackett-Luce model, where the data consists of full rankings over alternatives. Our technique is based on breaking the full rankings into pairwise comparisons, and then computing parameters that satisfy a set of generalized moment conditions. We identify conditions for the output of GMM to be unique, and identify a general class of consistent and inconsistent breakings. We then show by theory and experiments that our algorithms run significantly faster than the classical Minorize-Maximization (MM) algorithm, while achieving competitive statistical efficiency.

Both the path integral measure in field theory and ensembles of neural networks describe distributions over functions. When the central limit theorem can be applied in the infinite-width (infinite-$N$) limit, the ensemble of networks corresponds to a free field theory. Although an expansion in $1/N$ corresponds to interactions in the field theory, others, such as in a small breaking of the statistical independence of network parameters, can also lead to interacting theories. These other expansions can be advantageous over the $1/N$-expansion, for example by improved behavior with respect to the universal approximation theorem. Given the connected correlators of a field theory, one can systematically reconstruct the action order-by-order in the expansion parameter, using a new Feynman diagram prescription whose vertices are the connected correlators. This method is motivated by the Edgeworth expansion and allows one to derive actions for neural network field theories. Conversely, the correspondence allows one to engineer architectures realizing a given field theory by representing action deformations as deformations of neural network parameter densities. As an example, $\phi^4$ theory is realized as an infinite-$N$ neural network field theory.

Using machine learning along with data from wave tank experiments, MIT engineers have found a way to model how waves break. "With this, you could simulate waves to help design structures better, more efficiently, and without huge safety factors," says Themis Sapsis. The new model's predictions should help researchers improve ocean climate simulations and hone the design of offshore structures. Waves break once they swell to a critical height, before cresting and crashing into a shower of droplets and bubbles. These waves can be as big as a surfer's point break and as small as a gentle ripple rolling to shore.

Across the world, companies are in a fierce battle to assemble the right mix of hardware and software technology to deliver autonomous vehicles. With dozens of companies working on one of the hardest problems, driverless vehicles will be here in the not too distant future. So how is Australia getting ready to facilitate their introduction and allow businesses and citizens to take advantage of the technology? Last Friday, the 16th meeting of Infrastructure and Transport Ministers was held and they have made a really important determination, available in the now-public documents at infrastruture.gov.au Automated vehicles Ministers agreed that the future Automated Vehicle Safety Law will be implemented through Commonwealth law.

We propose a novel and flexible rank-breaking-then-composite-marginal-likelihood (RBCML) framework for learning random utility models (RUMs), which include the Plackett-Luce model. We characterize conditions for the objective function of RBCML to be strictly log-concave by proving that strict log-concavity is preserved under convolution and marginalization. We characterize necessary and sufficient conditions for RBCML to satisfy consistency and asymptotic normality. Experiments on synthetic data show that RBCML for Gaussian RUMs achieves better statistical efficiency and computational efficiency than the state-of-the-art algorithm and our RBCML for the Plackett-Luce model provides flexible tradeoffs between running time and statistical efficiency.

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The mainstream media is trying to make it look like Donald Trump has no chance of beating Hillary Clinton on Election Day in two weeks. That's why it came as a massive shock to many when an artificial intelligence (AI) system that correctly predicted the last three elections puts Trump ahead of Clinton in the race. According to The Gateway Pundit, the system found that enthusiasm for Trump is higher than the numbers Barack Obama had in 2008. Trump has overtaken Obama's popularity that year by a whopping 25%. CNBC reported that the system is called MogIA, and it was founded by Sanjiv Rai, the founder of Indian start-up Genic.ai.

In this paper we propose a class of efficient Generalized Method-of-Moments(GMM) algorithms for computing parameters of the Plackett-Luce model, where the data consists of full rankings over alternatives. Our technique is based on breaking the full rankings into pairwise comparisons, and then computing parameters that satisfy a set of generalized moment conditions. We identify conditions for the output of GMM to be unique, and identify a general class of consistent and inconsistent breakings. We then show by theory and experiments that our algorithms run significantly faster than the classical Minorize-Maximization (MM) algorithm, while achieving competitive statistical efficiency.