jm + models   4

Research Blog: Federated Learning: Collaborative Machine Learning without Centralized Training Data
Great stuff from Google - this is really nifty stuff for large-scale privacy-preserving machine learning usage:

It works like this: your device downloads the current model, improves it by learning from data on your phone, and then summarizes the changes as a small focused update. Only this update to the model is sent to the cloud, using encrypted communication, where it is immediately averaged with other user updates to improve the shared model. All the training data remains on your device, and no individual updates are stored in the cloud.

Federated Learning allows for smarter models, lower latency, and less power consumption, all while ensuring privacy. And this approach has another immediate benefit: in addition to providing an update to the shared model, the improved model on your phone can also be used immediately, powering experiences personalized by the way you use your phone.

Papers:
https://arxiv.org/pdf/1602.05629.pdf , https://arxiv.org/pdf/1610.05492.pdf
google  ml  machine-learning  training  federated-learning  gboard  models  privacy  data-privacy  data-protection 
7 weeks ago by jm
Combining static model checking with dynamic enforcement using the Statecall Policy Language
This looks quite nice -- a model-checker "for regular programmers". Example model for ping(1):

<pre>01 automaton ping (int max_count, int count, bool can_timeout) {
02 Initialize;
03 during {
04 count = 0;
05 do {
06 Transmit_Ping;
07 either {
08 Receive_Ping;
09 } or (can_timeout) {
10 Timeout_Ping;
11 };
12 count = count + 1;
13 } until (count &gt;= max_count);
14 } handle {
15 SIGINFO;
16 Print_Summary;
17 };</pre>
ping  model-checking  models  formal-methods  verification  static  dynamic  coding  debugging  testing  distcomp  papers 
march 2015 by jm
CausalImpact: A new open-source package for estimating causal effects in time series
How can we measure the number of additional clicks or sales that an AdWords campaign generated? How can we estimate the impact of a new feature on app downloads? How do we compare the effectiveness of publicity across countries?

In principle, all of these questions can be answered through causal inference.

In practice, estimating a causal effect accurately is hard, especially when a randomised experiment is not available. One approach we've been developing at Google is based on Bayesian structural time-series models. We use these models to construct a synthetic control — what would have happened to our outcome metric in the absence of the intervention. This approach makes it possible to estimate the causal effect that can be attributed to the intervention, as well as its evolution over time.

We've been testing and applying structural time-series models for some time at Google. For example, we've used them to better understand the effectiveness of advertising campaigns and work out their return on investment. We've also applied the models to settings where a randomised experiment was available, to check how similar our effect estimates would have been without an experimental control.

Today, we're excited to announce the release of CausalImpact, an open-source R package that makes causal analyses simple and fast. With its release, all of our advertisers and users will be able to use the same powerful methods for estimating causal effects that we've been using ourselves.

Our main motivation behind creating the package has been to find a better way of measuring the impact of ad campaigns on outcomes. However, the CausalImpact package could be used for many other applications involving causal inference. Examples include problems found in economics, epidemiology, or the political and social sciences.
causal-inference  r  google  time-series  models  bayes  adwords  advertising  statistics  estimation  metrics 
september 2014 by jm
Sweden Solar System
the world's largest permanent scale model of the Solar System. The Sun is represented by the Ericsson Globe in Stockholm, the largest hemispherical building in the world. The inner planets can also be found in Stockholm but the outer planets are situated northward in other cities along the Baltic Sea. The system was started by Nils Brenning and Gösta Gahm and is on the scale of 1:20 million.


(via JK)
scale  models  solar-system  astronomy  sun  sweden  science  cool  via:jk 
august 2014 by jm

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