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gwern branwen
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Most A/B experiments fail and the successful ones have small effects on the order of a few % at most. "What works in e-commerce - a meta-analysis of 6700 online experiments", Brown & Jones 2017:

"We conduct a meta-analysis on over 6700 large e-commerce experiments, mainly from the retail and travel sectors, grouping together common treatment types performed on websites. We find that cosmetic changes have a far smaller impact on revenue than treatments grounded in behavioural psychology. This research was independently assured by PricewaterhouseCoopers UK LLP"

Note the implications that most successful A/B tests will grossly overestimate the true effect size; detecting realistic effects may require large sample sizes; and some categories of tests may well not be worth running at all.

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Airline ticket search is not just NP-hard or worse, but undecidable: "Computational Complexity of Air Travel Planning", De Marcken 2003 (ITA Software).

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Transfer/multi-modality learning: "One Model To Learn Them All", Kaiser et al 2017:

"Deep learning yields great results across many fields, from speech recognition, image classification, to translation. But for each problem, getting a deep model to work well involves research into the architecture and a long period of tuning. We present a single model that yields good results on a number of problems spanning multiple domains. In particular, this single model is trained concurrently on ImageNet, multiple translation tasks, image captioning (COCO dataset), a speech recognition corpus, and an English parsing task. Our model architecture incorporates building blocks from multiple domains. It contains convolutional layers, an attention mechanism, and sparsely-gated layers. Each of these computational blocks is crucial for a subset of the tasks we train on. Interestingly, even if a block is not crucial for a task, we observe that adding it never hurts performance and in most cases improves it on all tasks. We also show that tasks with less data benefit largely from joint training with other tasks, while performance on large tasks degrades only slightly if at all."

Impressive. Not SOTA, but steps toward a generic NN which can do zero-shot, one-shot, or just plain learn faster by drawing on informative priors from other domains. One reason that deep NNs are data-hungry is that they can't borrow information from other domains, but that's never been fundamental - after all, humans have good finite-sample performance in part thanks to drawing on prior knowledge from all other tasks. And computer NNs don't 'age'.

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"Environmental factors dominate over host genetics in shaping human gut microbiota composition", Rothschild et al 2017

"Human gut microbiome composition is shaped by multiple host intrinsic and extrinsic factors, but the relative contribution of host genetic compared to environmental factors remains elusive. Here, we genotyped a cohort of 696 healthy individuals from several distinct ancestral origins and a relatively common environment, and demonstrate that there is no statistically significant association between microbiome composition and ethnicity, single nucleotide polymorphisms (SNPs), or overall genetic similarity, and that only 5 of 206 (2.5%) previously reported microbiome-SNP associations replicate in our cohort. In contrast, we find similarities in the microbiome composition of genetically unrelated individuals who share a household. We define the term biome-explainability as the variance of a host phenotype explained by the microbiome after accounting for the contribution of human genetics. Consistent with our finding that microbiome and host genetics are largely independent, we find significant biome-explainability levels of 16-33% for body mass index (BMI), fasting glucose, high-density lipoprotein (HDL) cholesterol, waist circumference, waist-hip ratio (WHR), and lactose consumption. We further show that several human phenotypes can be predicted substantially more accurately when adding microbiome data to host genetics data, and that the contribution of both data sources to prediction accuracy is largely additive. Overall, our results suggest that human microbiome composition is dominated by environmental factors rather than by host genetics."

Commentary: https://www.reddit.com/r/Microbiome/comments/6hojbc/environmental_factors_dominate_over_host_genetics/

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Recent human evolution: Selection for education/intelligence in East Asian populations: "Detecting polygenic adaptation in admixture graphs", Racimo et al 2017:

"An open question in human evolution is the importance of polygenic adaptation: adaptive changes in the mean of a multifactorial trait due to shifts in allele frequencies across many loci. In recent years, several methods have been developed to detect polygenic adaptation using loci identified in genome-wide association studies (GWAS). Though powerful, these methods suffer from limited interpretability: they can detect which sets of populations have evidence for polygenic adaptation, but are unable to reveal where in the history of multiple populations these processes occurred. To address this, we created a method to detect polygenic adaptation in an admixture graph, which is a representation of the historical divergences and admixture events relating different populations through time. We developed a Markov chain Monte Carlo (MCMC) algorithm to infer branch-specific parameters reflecting the strength of selection in each branch of a graph. Additionally, we developed a set of summary statistics that are fast to compute and can indicate which branches are most likely to have experienced polygenic adaptation. We show via simulations that this method - which we call PolyGraph - has good power to detect polygenic adaptation, and applied it to human population genomic data from around the world. We also provide evidence that variants associated with several traits, including height, educational attainment, and self-reported unibrow, have been influenced by polygenic adaptation in different human populations."

No sign of selection is found for Europeans, but if I understand the method right, it's only looking at net selection with time steps corresponding to populations branching. So given the European pattern of selection for intelligence at least up until agriculture and then heavy recent dysgenics against education/intelligence, those might mostly cancel out compared to an East Asian population like Japan.

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"Lithium in Drinking Water and Incidence of Suicide: A Nationwide Individual-Level Cohort Study with 22 Years of Follow-Up", Knudsen et al 2017:

"Suicide is a major public health concern. High-dose lithium is used to stabilize mood and prevent suicide in patients with affective disorders. Lithium occurs naturally in drinking water worldwide in much lower doses, but with large geographical variation. Several studies conducted at an aggregate level have suggested an association between lithium in drinking water and a reduced risk of suicide; however, a causal relation is uncertain. Individual-level register-based data on the entire Danish adult population (3.7 million individuals) from 1991 to 2012 were linked with a moving five-year time-weighted average (TWA) lithium exposure level from drinking water hypothesizing an inverse relationship. The mean lithium level was 11.6 μg/L ranging from 0.6 to 30.7 μg/L. The suicide rate decreased from 29.7 per 100,000 person-years at risk in 1991 to 18.4 per 100,000 person-years in 2012. We found no significant indication of an association between increasing five-year TWA lithium exposure level and decreasing suicide rate. The comprehensiveness of using individual-level data and spatial analyses with 22 years of follow-up makes a pronounced contribution to previous findings. Our findings demonstrate that there does not seem to be a protective effect of exposure to lithium on the incidence of suicide with levels below 31 μg/L in drinking water."

This is probably the best study of lithium/drinking-water/suicide ever done: large individual-level data on a comprehensive national level with good water measurements and decent statistics. But while the restriction of range is unfortunate, there's not even a hint of higher lithium levels reducing suicide despite precise estimates.

I would have liked to see them also analyze violence, crime and other mental illnesses, and maybe a family-level study since that's usually possible with a Scandinavian population registry study, but regardless, a major blow to the lithium hypothesis.

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"Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks", Katz et al 2017:

"Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. We present a novel, scalable, and efficient technique for verifying properties of deep neural networks (or providing counter-examples). The technique is based on the simplex method, extended to handle the non-convex Rectified Linear Unit (ReLU) activation function, which is a crucial ingredient in many modern neural networks. The verification procedure tackles neural networks as a whole, without making any simplifying assumptions. We evaluated our technique on a prototype deep neural network implementation of the next-generation airborne collision avoidance system for unmanned aircraft (ACAS Xu). Results show that our technique can successfully prove properties of networks that are an order of magnitude larger than the largest networks verified using existing methods."

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More recent human evolution: "Widespread signatures of positive selection in common risk alleles associated to autism spectrum disorder", Polimanti & Gelernter 2017:

"Predisposition to psychiatric disorders is due to the contribution of many genes involved in numerous molecular mechanisms. Since brain evolution has played a pivotal role in determining the success of the human species, the molecular pathways involved with the onset of mental illnesses are likely to be informative as we seek an understanding of the mechanisms involved in the evolution of human brain. Accordingly, we tested whether the genetics of psychiatric disorders is enriched for signatures of positive selection. We observed a strong finding related to the genetics of autism spectrum disorders (ASD): common risk alleles are enriched for genomic signatures of incomplete selection (loci where a selected allele has not yet reached fixation). The genes where these alleles map tend to be expressed in brain and pituitary tissues, to be involved in molecular mechanisms related to nervous system development, and surprisingly, to be associated with increased cognitive ability. Previous studies identified signatures of purifying selection in genes affected by ASD rare alleles. Accordingly, at least two different evolutionary mechanisms appear to be present in relation to ASD genetics: 1) rare disruptive alleles eliminated by purifying selection; 2) common alleles selected for their beneficial effects on cognitive skills. This scenario would explain ASD prevalence, which is higher than that expected for a trait under purifying selection, as the evolutionary cost of polygenic adaptation related to cognitive ability."
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