push first version

AUTHOR.txt

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+Copyright 2018 (Institution) under XAI Project supported by Ministry of Science and ICT, Korea
+
+# This is the list of (Institution) for copyright purposes.
+# This does not necessarily list everyone who has contributed code, since in
+# some cases, their employer may be the copyright holder.  To see the full list 
+# of contributors, see the revision history in source control

README.md

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-# Relational-Automatic-Statistician
-Expainable Baysian Models for Smooth Time Series Datasets (modified)
+Relational Automatic Statistician
+=====================
+
+Note that, this software is based on the automatic statistician system, http://www.automaticstatistician.com/index/.
+[https://github.com/jamesrobertlloyd/gpss-research](https://github.com/jamesrobertlloyd/gpss-research).
+
+This repository provides the source codes for the paper.
+
+[Automatic Construction of Nonparametric Relational Regression Models for
+Multiple Time Series](http://jmlr.org/proceedings/papers/v48/hwangb16.pdf) 
+by Yunseong Hwang, Anh Tong, Jaesik Choi
+in [ICML-2016](http://icml.cc/2016/)
+
+### Abstract
+
+Gaussian Processes (GPs) provide a general and analytically tractable way of modeling complex time-varying, nonparametric functions. The Automatic Bayesian Covariance Discovery (ABCD) system constructs natural-language description of time-series data by treating unknown time-series data nonparametrically using GP with a composite covariance kernel function. Unfortunately, learning a composite covariance kernel with a single time-series data set often results in less informative kernel that may not give qualitative, distinctive descriptions of data. We address this challenge by proposing two relational kernel learning methods which can model multiple time-series data sets by finding common, shared causes of changes. We show that the relational kernel learning methods find more accurate models for regression problems on several real-world data sets; US stock data, US house price index data and currency exchange rate data.
+
+This version of software is developed by Yunseong Hwang, Anh Tong and Jaesik Choi, members of [Statistical Artificial Intelligence Laboratory (SAIL)](http://sail.unist.ac.kr) at Ulsan National Institute of Science and Technology (UNIST), Korea. 
+
+If you have any question, Feel free to email the authors with any questions:  
+
+[Yunseong Hwang]() (yunseong.hwang@navercorp.com)  
+[Anh Tong]() (anhth@unist.ac.kr)  
+[Jaesik Choi]() (jaesik@unist.ac.kr)  
+
+### Reference
+- James Robert Lloyd, David Duvenaud, Roger Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani, Automatic Construction and Natural-Language Description of Nonparametric Regression Models, Association for the Advancement of Artificial Intelligence (AAAI) Conference, 2014.
+
+<img src="http://xai.unist.ac.kr/static/img/logos/XAIC_logo.png" width="300" height="100">
+
+# XAI Project 
+
+### **Project Name** 
+> A machine learning and statistical inference framework for explainable artificial intelligence(의사결정 이유를 설명할 수 있는 인간 수준의 학습·추론 프레임워크 개발)
+### **Managed by** 
+> Ministry of Science and ICT/XAIC
+### **Participated Affiliation** 
+> UNIST, Korean Univ., Yonsei Univ., KAIST., AItrics
+### **Web Site** 
+> <http://openXai.org>

license.txt

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+The MIT License (MIT)
+
+Copyright (c) 2014 by James Robert Lloyd, David Duvenaud and Roger Grosse.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.