Remove 2015 Remove Experimentation Remove Modeling Remove Risk
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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. That is true generally, not just in these experiments — spreading measurements out is generally better, if the straight-line model is a priori correct.

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Drug Discovery Needs AI To Discover More Treatments

Smart Data Collective

Current R&D Models Provide Diminishing Returns. In a report on the failure rates of drug discovery efforts between 2013 and 2015, Richard K. Without better methodology, difficult-to-treat and ill-understood conditions and diseases are at risk of staying that way. To understand why, we must first understand the process.

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

One reason to do ramp-up is to mitigate the risk of never before seen arms. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. For example, imagine a fantasy football site is considering displaying advanced player statistics.

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The 2015 Digital Marketing Rule Book. Change or Perish.

Occam's Razor

I've discovered that if we can just get them to imagine a better existence, undertake serious risks, experiment with new better ideas, and spend money executing them… they will ask for more robust measurement! Our mental model has not shifted enough to the existing reality. AND you can control for risk!

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Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. Using ML models to search more effectively brought the search space down to 102—which can run on modest hardware. For details, see their SIGMOD 2015 paper where Michael Armbrust & co. Model-Driven Data Queries.

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Themes and Conferences per Pacoid, Episode 6

Domino Data Lab

Spoiler alert: a research field called curiosity-driven learning is emerging at the nexis of experimental cognitive psychology and industry use cases for machine learning, particularly in gaming AI. The ability to measure results (risk-reducing evidence). Ensure a culture that supports a steady process of learning and experimentation.

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The trinity of errors in applying confidence intervals: An exploration using Statsmodels

O'Reilly on Data

Recall from my previous blog post that all financial models are at the mercy of the Trinity of Errors , namely: errors in model specifications, errors in model parameter estimates, and errors resulting from the failure of a model to adapt to structural changes in its environment. For example, if a stock has a beta of 1.4