Machine Learning and AI have the potential to transform data-driven scientific discovery, enabling accurate predictions for several scientific phenomena. As many scientific questions are inherently causal, this paper looks at the causal inference task of treatment effect estimation, where the outcome of interest is recorded in high-dimensional observations in a Randomized Controlled Trial (RCT). Despite being the simplest possible causal setting and a perfect fit for deep learning, we theoretically find that many common choices in the literature may lead to biased estimates. To test the practical impact of these considerations, we recorded ISTAnt, the first real-world benchmark for causal inference downstream tasks on high-dimensional observations as an RCT studying how garden ants (Lasius neglectus) respond to microparticles applied onto their colony members by hygienic grooming. Comparing 6 480 models fine-tuned from state-of-the-art visual backbones, we find that the sampling and modeling choices significantly affect the accuracy of the causal estimate, and that classification accuracy is not a proxy thereof. We further validated the analysis, repeating it on a synthetically generated visual data set controlling the causal model. Our results suggest that future benchmarks should carefully consider real downstream scientific questions, especially causal ones. Further, we highlight guidelines for representation learning methods to help answer causal questions in the sciences.

The hoard was likely buried sometime between 871 and 874 in present-day Worcestershire. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. One of the silver coins in the hands of their finder, moments after being found. Breakthroughs, discoveries, and DIY tips sent six days a week. In a classic example of lucky metal detectorists triggering an archaeological investigation, a group of metal detecting enthusiasts in England discovered a rare hoard of early medieval Anglo-Saxon coins in the parish of Bickmarsh, Worcestershire.

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Game Theory: Could geopolitics impact the business of sport in the Gulf? The Gulf helped transform global sport through billions in investment. But as geopolitical tensions rise is that era of rapid expansion coming to an end? Al Jazeera's Samantha Johnson looks at how geopolitics could impact the business of sport. The Masters: Golf's segregated past Are Iran's athletes political pawns?

Distributionally robust optimization has been shown to offer a principled way to regularize learning models. In this paper, we find that Tikhonov regularization is distributionally robust in an optimal transport sense (i.e., if an adversary chooses distributions in a suitable optimal transport neighborhood of the empirical measure), provided that suitable martingale constraints are also imposed. Further, we introduce a relaxation of the martingale constraints which not only provides a unified viewpoint to a class of existing robust methods but also leads to new regularization tools. To realize these novel tools, tractable computational algorithms are proposed. As a byproduct, the strong duality theorem proved in this paper can be potentially applied to other problems of independent interest.

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Kenyan runner Sabastian Sawe set a new men's marathon world record at the London Marathon, finishing in 1 hour, 59 minutes and 30 seconds on Sunday.