[{"id":21,"title":"Great stock or climate change indicator?","sha":"a05954037a4797343c7e8e6f16bcb193","user_id":28,"state":"published","body":"I propose to gather a collection of sock price histories and various indicators of climate change (temperatures, CO2 levels, etc.) and anonymize their labels. These would be used to create a game or prediction market so that the players would be incentivized to choose which of these time series they think show a positive trend. This blind labeling of climate change time series would be useful for providing input into the debates around particular time series that are heavily biased by the politics surrounding the issue.","created_at":"2015-02-09T06:05:27.725Z","updated_at":"2015-03-03T20:44:18.611Z","zenodo_id":21723,"doi":"http://dx.doi.org/10.5281/zenodo.14937","subject":null,"tags":["climate","change","stock","market"],"vote_count":3,"view_count":1,"score":3.1,"deleted":false,"muted":false,"tweeted":false,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":260,"title":"Brief Ideas for the Data Science at LHC Workshop 2015","sha":"48be7c9cc3de0c437011525e9d0b0180","user_id":null,"state":"published","body":"The Data Science @ LHC workshop was a resounding success. We do not plan to have traditional proceedings tied to individual talks, but we do want to capture the ideas that were generated during the workshop. With that in mind, we want to try something new: we encourage you to submit contributions to the Journal of Brief Ideas. Each contribution is capped at 200 words, so this provides a lightweight method of recording ideas that came up during the workshop. They do not have to be tied to a particular talk or speaker, and you can even submit several ideas with a different set of authors. The goal is to lay the groundwork for new collaborations and follow up studies, not to document completed research. As this is a new way of doing things, please let us know what you think or if need help with anything. Each publication will receive a DOI so that it can be cited by other publications and collected by INSPIRE as contributions to the conference.\r\nImportant : fill in your brief ideas by 22 January.\r\nVery important: please use this tag: DSLHC when composing the idea so that we can easily aggregate them.","created_at":"2015-12-22T22:54:51.146Z","updated_at":"2016-01-06T06:00:27.519Z","zenodo_id":92248,"doi":"http://dx.doi.org/10.5281/zenodo.44357","subject":null,"tags":["datascience","lhc","dslhc"],"vote_count":0,"view_count":1,"score":0.1,"deleted":false,"muted":false,"tweeted":false,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":285,"title":"Create standalone simulation tools to facilitate collaboration between HEP and machine learning community","sha":"ff0489d51bdb17359cef823c1d6b7029","user_id":null,"state":"published","body":"Discussions at recent workshops have made it clear that one of the key barriers to collaboration between high energy physics and the machine learning community is access to training data. Recent successes in data sharing through the [HiggsML](http://doi.org/10.7483/OPENDATA.ATLAS.ZBP2.M5T8) and [Flavours of Physics](https://www.kaggle.com/c/flavours-of-physics/data) Kaggle challenges have borne much fruit, but required significant effort to coordinate.\r\n\r\nWhile static simulated datasets are useful for challenges, in the course of investigating new machine learning techniques it is advantageous to be able to generate training data on demand (e.g. Refs. [1](https://archive.ics.uci.edu/ml/datasets/HEPMASS), [2](https://archive.ics.uci.edu/ml/datasets/SUSY), [3](https://archive.ics.uci.edu/ml/datasets/HIGGS) ). \r\nTherefore we recommend efforts be made to produce the ingredients required to facilitate such collaboration:\r\n   * Specific challenges for HEP experiments should be fully specified such that minimal domain-specific knowledge is required to attack them.\r\n   * Stand-alone simulators should be made open source. They should be developed to be easy to use without domain-specific expertise, while still being representative of real experimental challenges. Such a simulation will permit non-HEP researchers to generate realistic HEP datasets for training and testing. These simulators could range from truth-level simulation of a hard scattering to fast simulation like [Delphes](http://dx.doi.org/10.1007/JHEP02(2014)057), to full [GEANT4](http://dx.doi.org/10.1016/S0168-9002(03)01368-8) simulation of sensor arrays.\r\n   * Performance metrics (objective functions) and operational constraints should be defined to evaluate proposed solutions.\r\n","created_at":"2016-02-26T21:34:14.721Z","updated_at":"2016-04-03T17:04:01.541Z","zenodo_id":99761,"doi":"http://dx.doi.org/10.5281/zenodo.46864","subject":null,"tags":["dslhc","machinelearning","datascience","open data","simulation"],"vote_count":3,"view_count":1,"score":3.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":354,"title":"Unifying generative models and exact likelihood-free inference with conditional bijections","sha":"5c2f74aedbf3618ca180382e393c7617","user_id":null,"state":"published","body":"Recent work in density estimation uses a bijection $f : X \\to Z$ (e.g. an invertible flow or autoregressive model) and a tractable density $p(z)$  (e.g. [[1]](https://arxiv.org/abs/1410.8516) [[2]](http://www.dmi.usherb.ca/~larocheh/projects_nade.html) [[3]](https://arxiv.org/abs/1410.6460) [[4]](https://arxiv.org/abs/1505.05770)). \r\n\\begin{equation}\r\np(x) = p(f_\\phi(x)) \\left| \\det\\left ( \\frac{\\partial f_\\phi(x)}{\\partial x_T} \\right) \\right | \\;,\r\n\\end{equation}\r\nwhere $\\phi$ are the internal network parameters for the bijection $f_\\phi$. Learning proceeds via gradient ascent $\\nabla_\\phi \\sum_i \\log p(x_i)$ with data $x_i$ (i.e. maximum likelihood wrt. the internal parameters $\\phi$). Since $f$ is invertible, then this model can also be used as a generative model for $X$.\r\n\r\nThis can be generalized to the conditional density $p(x|\\theta)$ by utilizing a family of bijections $f_{\\theta} : X \\to Z$ parametrized by $\\theta$ (e.g. [[5]](https://arxiv.org/pdf/1609.03499.pdf) [[6]](https://arxiv.org/abs/1611.05209)). \r\n\\begin{equation}\r\np(x|\\theta) = p(f_{\\phi; \\theta}(x)) \\left| \\det \\left ( \\frac{\\partial f_{\\phi; \\theta}(x)}{\\partial x_T} \\right) \\right |\r\n\\end{equation}\r\nHere $\\theta$ and $x$ are input to the network (and its inverse) and $\\phi$ are internal network parameters. Again, learning proceeds via gradient ascent $\\nabla_\\phi \\sum_i \\log p(x_i|\\theta_i)$ with data $x_i,\\theta_i$.\r\n\r\nWe observe that not only can this model be used as a conditional generative model $p(x|\\theta)$, but it can also be used to perform asymptotically exact, amortized likelihood-free inference on $\\theta$.\r\n\r\nThis is particularly interesting when $\\theta$ is identified with the parameters of an intractable, non-differentiable computer simulation or the conditions of some real world data collection process.","created_at":"2016-12-08T11:48:03.802Z","updated_at":"2016-12-10T02:41:10.507Z","zenodo_id":198541,"doi":"https://doi.org/10.5281/zenodo.198541","subject":null,"tags":["machine learning","likelihood-free inference","density estimation"],"vote_count":1,"view_count":1,"score":1.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":412,"title":"Recasting through reweighting","sha":"8106c030eba22dd3a8d268940d5e42d8","user_id":null,"state":"published","body":"*Recasting* refers to reinterpreting the results of searches for new particles or standard model measurements in the context of different theoretical models\u0026nbsp;\\[[1](http://inspirehep.net/record/872781)\\]. The fundamental task is to replace the original hypothesis $p_0(x)$ with a new hypothesis $p_1(x)$, where $x$ is some observed quantity. The effect of the detector response and analysis cuts can be encoded in a *folding* operator $\\int\\,W(x|z)dz$ acting on the truth-level distribution $p(z)$. By keeping the  analysis fixed, $W(x|z)$ does not change, thus recasting amounts to:\r\n\\begin{equation}p_0(x)=\\int\\,p_0(z)W(x|z)dz\\hspace{.5cm}\\Longrightarrow\\hspace{.5cm}p_1(x)=\\int\\,p_1(z)W(x|z)dz\\end{equation}\r\n\r\nThere are two primary approaches:\r\n  * **folding**: Samples from $p_1(z)$ are run through a detector simulation and analysis chain to estimate $p_1(x)$\u0026nbsp;\\[[2](https://indico.cern.ch/event/567550/contributions/2638695/)\\]. This is common when $z$ is high-dimensional, $p_0(z)$ and $p_1(z)$ are very different, or $W(x|z)$ is sensitive to experimental details.\r\n  * **unfolding**: An alternate theory $p_1(z)$ is compared directly to an unfolded distribution $\\hat{p}(z)$ obtained from applying an approximate inverse operation to the observed data. Typically, unfolding is restricted to low-dimensional $x, z$ and Gaussian uncertainties.\r\n\r\nWe point out a third option\r\n   * **reweighting**: Reweight pre-folded events $(x_i,z_i)~\\sim\\,p_0(x,z)$ by the factor $r(z_i)=p_1(z_i)/p_0(z_i)$, as in\r\n\\begin{equation}p_1(x)=\\int p_1(z)W(x|z)dz=\\int\\,p_0(z)\\underbrace{\\frac{p_1(z)}{p_0(z)}}_{\\textrm{reweighting}}W(x|z)dz\\end{equation}\r\nThis approach does not require simulating new events or the approximations used in unfolding. Note, sample variance becomes a problem if $r(z_i)\\gg\\,1$.","created_at":"2017-10-14T14:16:18.901Z","updated_at":"2022-06-05T20:51:32.689Z","zenodo_id":1013926,"doi":"https://doi.org/10.5281/zenodo.1013926","subject":null,"tags":["physics","reinterpretation","lhc"],"vote_count":3,"view_count":1,"score":3.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null}]