[docs] Unified references and fixed typo (#1695)

* unified references

* fixed typo

* GPU version is quite stable now

* updated reference for GPU version

CMakeLists.txt

@@ -8,7 +8,7 @@ PROJECT(lightgbm)
 
 OPTION(USE_MPI "MPI based parallel learning" OFF)
 OPTION(USE_OPENMP "Enable OpenMP" ON)
-OPTION(USE_GPU "Enable GPU-acclerated training (EXPERIMENTAL)" OFF)
+OPTION(USE_GPU "Enable GPU-accelerated training" OFF)
 OPTION(USE_SWIG "Enable SWIG to generate Java API" OFF)
 OPTION(USE_HDFS "Enable HDFS support (EXPERIMENTAL)" OFF)
 OPTION(USE_R35 "Set to ON if your R version is not smaller than 3.5" OFF)

README.md

@@ -109,8 +109,8 @@ This project has adopted the [Microsoft Open Source Code of Conduct](https://ope
 Reference Papers
 ----------------
 
-Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. "[LightGBM: A Highly Efficient Gradient Boosting Decision Tree](https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree)". In Advances in Neural Information Processing Systems (NIPS), pp. 3149-3157. 2017.
+Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu. "[LightGBM: A Highly Efficient Gradient Boosting Decision Tree](https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree)". Advances in Neural Information Processing Systems 30 (NIPS 2017), pp. 3149-3157.
 
-Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tieyan Liu. "[A Communication-Efficient Parallel Algorithm for Decision Tree](http://papers.nips.cc/paper/6380-a-communication-efficient-parallel-algorithm-for-decision-tree)". Advances in Neural Information Processing Systems 29 (NIPS 2016).
+Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tie-Yan Liu. "[A Communication-Efficient Parallel Algorithm for Decision Tree](http://papers.nips.cc/paper/6380-a-communication-efficient-parallel-algorithm-for-decision-tree)". Advances in Neural Information Processing Systems 29 (NIPS 2016), pp. 1279-1287.
 
-Huan Zhang, Si Si and Cho-Jui Hsieh. "[GPU Acceleration for Large-scale Tree Boosting](https://arxiv.org/abs/1706.08359)". arXiv:1706.08359, 2017.
+Huan Zhang, Si Si and Cho-Jui Hsieh. "[GPU Acceleration for Large-scale Tree Boosting](https://arxiv.org/abs/1706.08359)". SysML Conference, 2018.

docs/Features.rst

@@ -257,7 +257,7 @@ For more details, please refer to `Parameters <./Parameters.rst>`__.
 References
 ----------
 
-[1] Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. "`LightGBM\: A Highly Efficient Gradient Boosting Decision Tree`_." In Advances in Neural Information Processing Systems (NIPS), pp. 3149-3157. 2017.
+[1] Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu. "`LightGBM\: A Highly Efficient Gradient Boosting Decision Tree`_." Advances in Neural Information Processing Systems 30 (NIPS 2017), pp. 3149-3157.
 
 [2] Mehta, Manish, Rakesh Agrawal, and Jorma Rissanen. "SLIQ: A fast scalable classifier for data mining." International Conference on Extending Database Technology. Springer Berlin Heidelberg, 1996.
 
@@ -267,17 +267,17 @@ References
 
 [5] Machado, F. P. "Communication and memory efficient parallel decision tree construction." (2003).
 
-[6] Li, Ping, Qiang Wu, and Christopher J. Burges. "Mcrank: Learning to rank using multiple classification and gradient boosting." Advances in neural information processing systems. 2007.
+[6] Li, Ping, Qiang Wu, and Christopher J. Burges. "Mcrank: Learning to rank using multiple classification and gradient boosting." Advances in Neural Information Processing Systems 20 (NIPS 2007).
 
 [7] Shi, Haijian. "Best-first decision tree learning." Diss. The University of Waikato, 2007.
 
 [8] Walter D. Fisher. "`On Grouping for Maximum Homogeneity`_." Journal of the American Statistical Association. Vol. 53, No. 284 (Dec., 1958), pp. 789-798.
 
-[9] Thakur, Rajeev, Rolf Rabenseifner, and William Gropp. "`Optimization of collective communication operations in MPICH`_." International Journal of High Performance Computing Applications 19.1 (2005): 49-66.
+[9] Thakur, Rajeev, Rolf Rabenseifner, and William Gropp. "`Optimization of collective communication operations in MPICH`_." International Journal of High Performance Computing Applications 19.1 (2005), pp. 49-66.
 
-[10] Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tieyan Liu. "`A Communication-Efficient Parallel Algorithm for Decision Tree`_." Advances in Neural Information Processing Systems 29 (NIPS 2016).
+[10] Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tie-Yan Liu. "`A Communication-Efficient Parallel Algorithm for Decision Tree`_." Advances in Neural Information Processing Systems 29 (NIPS 2016), pp. 1279-1287.
 
-[11] Huan Zhang, Si Si and Cho-Jui Hsieh. "`GPU Acceleration for Large-scale Tree Boosting`_." arXiv:1706.08359, 2017.
+[11] Huan Zhang, Si Si and Cho-Jui Hsieh. "`GPU Acceleration for Large-scale Tree Boosting`_." SysML Conference, 2018.
 
 .. _LightGBM\: A Highly Efficient Gradient Boosting Decision Tree: https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf
 

docs/GPU-Performance.rst

@@ -194,7 +194,7 @@ Further Reading
 You can find more details about the GPU algorithm and benchmarks in the
 following article:
 
-Huan Zhang, Si Si and Cho-Jui Hsieh. `GPU Acceleration for Large-scale Tree Boosting`_. arXiv:1706.08359, 2017.
+Huan Zhang, Si Si and Cho-Jui Hsieh. `GPU Acceleration for Large-scale Tree Boosting`_. SysML Conference, 2018.
 
 .. _link1: https://archive.ics.uci.edu/ml/datasets/HIGGS
 

docs/GPU-Tutorial.rst

@@ -188,7 +188,7 @@ Reference
 
 Please kindly cite the following article in your publications if you find the GPU acceleration useful:
 
-Huan Zhang, Si Si and Cho-Jui Hsieh. "`GPU Acceleration for Large-scale Tree Boosting`_." arXiv:1706.08359, 2017.
+Huan Zhang, Si Si and Cho-Jui Hsieh. "`GPU Acceleration for Large-scale Tree Boosting`_." SysML Conference, 2018.
 
 .. _Microsoft Azure cloud computing platform: https://azure.microsoft.com/