**Northstar**

Northstar is an interactive data science platform that rethinks how people interact with data. It empowers users without programming experience, background in statistics or machine learning expertise to explore and mine data through an intuitive user interface, and effortlessly build, analyze, and evaluate machine learning (ML) pipelines. The system provides a fundamentally re-thought data analytics stack with innovations across three areas: User Interface, Interactive Data Exploration Accelerator and Automated ML. … **Forward Slice**

We propose a method for stochastic optimization: ‘Forward Slice’. We evaluate its performance and apply to design problems in Section 3. At its core, our method is based on the procedure that Neal (2003) called the `slice sampling’ procedure , which was originally developed as a Markov chain Monte Carlo sampling procedure to draw samples from a target distribution. The slice sampling method relies on an auxiliary variable which de nes a level at which we slice the target density to obtain regions from which we draw samples of the target distribution. Similar to Neal’s method, our procedure uses an auxiliary variable for stochastic optimization that also de nes the slices, but of an objective function to be maximized (or minimized). Moreover, unlike with Neal’s method, the auxiliary variable in our approach is not sampled and takes on non-decreasing values in the sequential iterations of the procedure so that, for a given pre{speci ed tolerance, at the end of the procedure we attain the maxima and the argument of the maxima (or close values given the selected tolerance level). … **Likelihood-Ratio Test (LRT)**

In statistics, a likelihood ratio test is a statistical test used to compare the fit of two models, one of which (the null model) is a special case of the other (the alternative model). The test is based on the likelihood ratio, which expresses how many times more likely the data are under one model than the other. This likelihood ratio, or equivalently its logarithm, can then be used to compute a p-value, or compared to a critical value to decide whether to reject the null model in favour of the alternative model. When the logarithm of the likelihood ratio is used, the statistic is known as a log-likelihood ratio statistic, and the probability distribution of this test statistic, assuming that the null model is true, can be approximated using Wilks’s theorem. In the case of distinguishing between two models, each of which has no unknown parameters, use of the likelihood ratio test can be justified by the Neyman-Pearson lemma, which demonstrates that such a test has the highest power among all competitors. … **LDMI**

Word2Vec’s Skip Gram model is the current state-of-the-art approach for estimating the distributed representation of words. However, it assumes a single vector per word, which is not well-suited for representing words that have multiple senses. This work presents LDMI, a new model for estimating distributional representations of words. LDMI relies on the idea that, if a word carries multiple senses, then having a different representation for each of its senses should lead to a lower loss associated with predicting its co-occurring words, as opposed to the case when a single vector representation is used for all the senses. After identifying the multi-sense words, LDMI clusters the occurrences of these words to assign a sense to each occurrence. Experiments on the contextual word similarity task show that LDMI leads to better performance than competing approaches. …

# If you did not already know

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Jun 2020

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