cslt Wiki - 用户贡献 [zh-cn]

FIN Status Report 2016-09-05

2016-09-05T05:17:08Z

Wangmn：

{| class="wikitable"
!People !! Last Week !! This Week
|-
|Caixia Wang
||

||

|-

|-
| Maoning Wang||
seminar-notes:狄利克雷过程（狄利克雷过程的定义、狄利克雷过程的构造——折筷子、G的后验概率）
||
seminar-notes:狄利克雷过程（预测概率分布与中国餐馆问题）、演化算法（简介、例子与流程、表示方法、改进：粒子群算法）
|-

|-
| Yang Wang||
Do some experiments with DQNStock<br>
Read some papers about methods that improve the stability of the training<br>
Config computers for our group
||
continue to do experiments
|-

|}

FIN Status Report 2016-08-29

2016-08-29T06:01:32Z

Wangmn：

{| class="wikitable"
!People !! Last Week !! This Week
|-
|Caixia Wang
||
* Finished Chapter 5.
* Have been writing Chapter 10.
||
* Continue to write Chapter 10.
|-

|-
| Maoning Wang||
* seminar-notes: 高斯过程（回顾贝叶斯线性回归、高斯过程、高斯过程回归）、狄利克雷过程（回顾高斯混合模型）
||
* seminar-notes:狄利克雷过程（狄利克雷过程的定义、狄利克雷过程的构造——折筷子、G的后验概率、预测概率分布与中国餐馆问题）
|-

|-
| Yang Wang||
* CONTENT
||
* CONTENT
|-

|}

FIN Status Report 2016-08-22

2016-08-22T07:33:31Z

Wangmn：

{| class="wikitable"
!People !! Last Week !! This Week
|-
|第一个人 ||
*
*
||
*
|-
|Maoning Wang ||
*read materials on Gaussian Processes and Dirichlet Process
*put down the lecture notes: Introduction--completed, Gaussian Processes--only a little is completed
||
*continue taking the summer-seminar notes
|-
|添加姓名 || ||
|}

FIN Status Report 2016-08-22

2016-08-22T07:31:46Z

Wangmn：

FIN Status Report 2016-08-22

2016-08-22T07:31:14Z

Wangmn：以“ {| class="wikitable" !People !! Last Week !! This Week |- |第一个人 || * * || * |- |Maoning Wang || *read materials on Gaussian Processes and Dirichlet Process...”为内容创建页面

2016-08-22

2016-08-22T07:21:56Z

Wangmn：

[[NLP Status Report 2016-08-22]]

[[ASR Status Report 2016-08-22]]

[[FIN Status Report 2016-08-22]]

Maoning Wang 2016-08-15

2016-08-14T19:02:44Z

Wangmn：以“last week: 1.learn how to use the EA software/lib OpenBeagle(C++) and GPlab(Matlab) 2.read the paper "GP+PSO" this week: 1.consider whether the SHD(structural ha...”为内容创建页面

last week:

1.learn how to use the EA software/lib OpenBeagle(C++) and GPlab(Matlab)

2.read the paper "GP+PSO"

this week:

1.consider whether the SHD(structural hammming distance) for trees can be extended to strongly-typed tree

2.implement the algorithms for finding trading rules with GP+PSO

2016-08-15

2016-08-14T18:52:48Z

Wangmn：

[[Chao Xing 2016-08-15]]

[[Maoning Wang 2016-08-15]]

2016-08-08 Maoning Wang

2016-08-07T19:23:36Z

Wangmn：以“last week: 1.read papers on evolving trading rules using genetic programming, learn several details on strongly-typed GP; 2.install Open BEAGLE (a C++ Evolutionary...”为内容创建页面

last week:

1.read papers on evolving trading rules using genetic programming, learn several details on strongly-typed GP;

2.install Open BEAGLE (a C++ Evolutionary Computation software) on the server

this week:

1.test the Open BEAGLE examples

2.consider GP+PSO

2016-08-08

2016-08-07T19:12:14Z

Wangmn：

[[2016-08-08 Mengyuan Zhao]]

[[2016-08-08 Chao Xing]]

[[2016-08-08 Maoning Wang]]

Maoning Wang 2016-08-01

2016-07-31T18:00:51Z

Wangmn：以“last week: 1.complete the presentation of the chapter "evolutionary computing" in the seminar 2.make an overview on how does evolutionary computing gives better tr...”为内容创建页面

last week:

1.complete the presentation of the chapter "evolutionary computing" in the seminar

2.make an overview on how does evolutionary computing gives better trading strategies

this week:

1.consider how to trim the operations in PSO for "trees with typed nodes/syntax tree"

2.implement the evolution of trading strategies with PSO/GP

2016-08-01

2016-07-31T17:45:08Z

Wangmn：

[[Chao Xing 2016-08-01]]

[[Lantian Li 2016-08-01]]

[[Andi Zhang 2016-08-01]]

[[Maoning Wang 2016-08-01]]

2016 Summer Seminar for Machine learning

2016-07-28T11:02:42Z

Wangmn：

*Location: FIT-1-304

{| class="wikitable"
! Date !! Speaker!! Title !! Owner !! Materials
|-
| 2016/07/04 ||Dong Wang || Machine learning overview || Dong Wang || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt1.%20Overview%20of%20Machine%20Learning.pptx slides][http://arch.cslt.org/video/2016/sum-ML/lesson1-2.mp4 video(part 2)][http://cs229.stanford.edu/section/cs229-linalg.pdf Algebra review] [http://cs229.stanford.edu/section/cs229-prob.pdf probability review] [http://cs229.stanford.edu/section/gaussians.pdf Gaussian distribution][http://cs229.stanford.edu/notes/cs229-notes4.pdf Learning theory]
|-
| 2016/07/05 ||Dong Wang || Linear models || Aiting Liu; Aodong Li|| [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt2.%20Linear%20Model.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson2-1.mp4 video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson2-2.mp4 video(part 2)] [http://cs229.stanford.edu/notes/cs229-notes1.pdf NG's lecture 1] [http://cs229.stanford.edu/notes/cs229-notes2.pdf NG's lecture 2]
|-
| 2016/07/08 ||Dong Wang || Neural networks || Aiting Liu || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt3.%20Neural%20Network.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson3-1.mp4 video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson3-2.mp4 video(part 2)]
|-
| 2016/07/11 ||Dong Wang || Deep learning (1)|| Hang Luo; Jiyuan Zhang; Zhiyuan|| [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt4.%20Deep%20learning-1.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson4-1_Deep_learning.m4v video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson4-2_Deep_learning.m4v video(part 2)] [http://www.iro.umontreal.ca/~bengioy/talks/DL-Tutorial-NIPS2015.pdf NIPS 2015 tutorial]
|-
| 2016/07/12 ||Dong Wang || Deep learning (2)|| Hang Luo; Jiyuan Zhang; Zhiyuan || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt5.%20Deep%20learning-2.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson5-1_Deep_learning.m4v video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson5-2_Deep_learning.m4v video(part 2)] [http://www.icassp2016.org/SP16_PlenaryDeng_Slides.pdf Li Deng's ICASSP16 keynote]
|-
| 2016/07/13 || Caixia Wang || Kernel methods || Caixia;Ziwei || [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/0/07/KM1.pdf slides] [http://arch.cslt.org/video/2016/sum-ML/lesson6_Kernel_method.m4v video] [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/b/b5/Kernel_Methods_for_Pattern_Analysis.pdf Kernel method book] [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/9/95/Pattern_Recognition_and_Machine_Learning.pdf pattern recognition 6-7]
|-
| 2016/07/18 ||Yang Feng || Graphical model (1) ||Jingyi Lin; Ying Shi; Yang Wang|| [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/d/dd/Chpt7._Graphical_models-bayesian_approach.pdf slides] [http://arch.cslt.org/video/2016/sum-ML/lesson7_Graphical_model.m4v video]
|-
| 2016/07/21 ||Dong Wang || Graphical model (2) ||Jingyi Lin; Ying Shi; Yang Wang||[http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt8.%20Graphical%20models-2.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson8-1_Graphical_model.m4v video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson8-2_Graphical_model.m4v video(part 2)] [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chapt8/Graphical%20models-learningInference.pptx Yang's slides] [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chapt8/GraphicalModel_Jordan.pdf Jordan's lecture]
|-
| 2016/07/25 ||Dong Wang || Unsupervised learning || ||[http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt9.%20Unsupervised%20Learning.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson9-1_Unsupervised_learning.m4v video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson9-2_Unsupervised_learning.m4v video(part 2)] [http://mlg.eng.cam.ac.uk/zoubin/papers/ul.pdf Unsupervised Learning from Zoubin Ghahramani, Cambridge] [http://www.mit.edu/~9.54/fall14/slides/Class13.pdf slides from MIT] [https://page.mi.fu-berlin.de/rojas/neural/chapter/K5.pdf Neural Networks - A Systematic Introduction, Raul Rojas] [http://www.cs.bu.edu/fac/gkollios/ada01/LectNotes/Clustering2.ppt slides from BU] [http://web.mit.edu/6.454/www/www_fall_2003/ihler/slides.pdf manifold slides from MIT]
|-
| 2016/07/26 ||Dong Wang || Non parametric models ||Maoning Wang; || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt10.%20Non-Parametric%20Learning.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson10-1_Nonparameteric_model.m4v video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson10-2_Nonparameteric_model.m4v video(part 2)] [http://cs229.stanford.edu/section/cs229-gaussian_processes.pdf Gaussian process] [http://stat.columbia.edu/~porbanz/npb-tutorial.html Resource for non-parametric Bayesian] [http://www.gatsby.ucl.ac.uk/~ywteh/research/npbayes/dp.pdf A good tutorial]
|-
| 2016/07/28 ||Maoning Wang || Evolutionary learning ||Maoning Wang; ||[http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/%E6%96%87%E4%BB%B6:EA_%E7%8E%8B%E5%8D%AF%E5%AE%81.pdf] [http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/%E6%96%87%E4%BB%B6:Introduction_to_Evolutionary_Computing.pdf]
|-
| 2016/07/29 ||Dong Wang || Reinforcement learning || Chao Xing ||
|-
| 2016/08/01 ||Dong Wang || Optimization || Caixia; || [http://cs229.stanford.edu/section/cs229-cvxopt.pdf Convex optimization I] [http://cs229.stanford.edu/section/cs229-cvxopt.pdf Convex optimization II]
|-
|}

文件:Introduction to Evolutionary Computing.pdf

2016-07-28T11:01:58Z

Wangmn：

2016 Summer Seminar for Machine learning

2016-07-28T11:00:28Z

Wangmn：

*Location: FIT-1-304

{| class="wikitable"
! Date !! Speaker!! Title !! Owner !! Materials
|-
| 2016/07/04 ||Dong Wang || Machine learning overview || Dong Wang || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt1.%20Overview%20of%20Machine%20Learning.pptx slides][http://arch.cslt.org/video/2016/sum-ML/lesson1-2.mp4 video(part 2)][http://cs229.stanford.edu/section/cs229-linalg.pdf Algebra review] [http://cs229.stanford.edu/section/cs229-prob.pdf probability review] [http://cs229.stanford.edu/section/gaussians.pdf Gaussian distribution][http://cs229.stanford.edu/notes/cs229-notes4.pdf Learning theory]
|-
| 2016/07/05 ||Dong Wang || Linear models || Aiting Liu; Aodong Li|| [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt2.%20Linear%20Model.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson2-1.mp4 video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson2-2.mp4 video(part 2)] [http://cs229.stanford.edu/notes/cs229-notes1.pdf NG's lecture 1] [http://cs229.stanford.edu/notes/cs229-notes2.pdf NG's lecture 2]
|-
| 2016/07/08 ||Dong Wang || Neural networks || Aiting Liu || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt3.%20Neural%20Network.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson3-1.mp4 video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson3-2.mp4 video(part 2)]
|-
| 2016/07/11 ||Dong Wang || Deep learning (1)|| Hang Luo; Jiyuan Zhang; Zhiyuan|| [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt4.%20Deep%20learning-1.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson4-1_Deep_learning.m4v video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson4-2_Deep_learning.m4v video(part 2)] [http://www.iro.umontreal.ca/~bengioy/talks/DL-Tutorial-NIPS2015.pdf NIPS 2015 tutorial]
|-
| 2016/07/12 ||Dong Wang || Deep learning (2)|| Hang Luo; Jiyuan Zhang; Zhiyuan || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt5.%20Deep%20learning-2.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson5-1_Deep_learning.m4v video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson5-2_Deep_learning.m4v video(part 2)] [http://www.icassp2016.org/SP16_PlenaryDeng_Slides.pdf Li Deng's ICASSP16 keynote]
|-
| 2016/07/13 || Caixia Wang || Kernel methods || Caixia;Ziwei || [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/0/07/KM1.pdf slides] [http://arch.cslt.org/video/2016/sum-ML/lesson6_Kernel_method.m4v video] [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/b/b5/Kernel_Methods_for_Pattern_Analysis.pdf Kernel method book] [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/9/95/Pattern_Recognition_and_Machine_Learning.pdf pattern recognition 6-7]
|-
| 2016/07/18 ||Yang Feng || Graphical model (1) ||Jingyi Lin; Ying Shi; Yang Wang|| [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/d/dd/Chpt7._Graphical_models-bayesian_approach.pdf slides] [http://arch.cslt.org/video/2016/sum-ML/lesson7_Graphical_model.m4v video]
|-
| 2016/07/21 ||Dong Wang || Graphical model (2) ||Jingyi Lin; Ying Shi; Yang Wang||[http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt8.%20Graphical%20models-2.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson8-1_Graphical_model.m4v video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson8-2_Graphical_model.m4v video(part 2)] [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chapt8/Graphical%20models-learningInference.pptx Yang's slides] [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chapt8/GraphicalModel_Jordan.pdf Jordan's lecture]
|-
| 2016/07/25 ||Dong Wang || Unsupervised learning || ||[http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt9.%20Unsupervised%20Learning.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson9-1_Unsupervised_learning.m4v video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson9-2_Unsupervised_learning.m4v video(part 2)] [http://mlg.eng.cam.ac.uk/zoubin/papers/ul.pdf Unsupervised Learning from Zoubin Ghahramani, Cambridge] [http://www.mit.edu/~9.54/fall14/slides/Class13.pdf slides from MIT] [https://page.mi.fu-berlin.de/rojas/neural/chapter/K5.pdf Neural Networks - A Systematic Introduction, Raul Rojas] [http://www.cs.bu.edu/fac/gkollios/ada01/LectNotes/Clustering2.ppt slides from BU] [http://web.mit.edu/6.454/www/www_fall_2003/ihler/slides.pdf manifold slides from MIT]
|-
| 2016/07/26 ||Dong Wang || Non parametric models ||Maoning Wang; || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt10.%20Non-Parametric%20Learning.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson10-1_Nonparameteric_model.m4v video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson10-2_Nonparameteric_model.m4v video(part 2)] [http://cs229.stanford.edu/section/cs229-gaussian_processes.pdf Gaussian process] [http://stat.columbia.edu/~porbanz/npb-tutorial.html Resource for non-parametric Bayesian] [http://www.gatsby.ucl.ac.uk/~ywteh/research/npbayes/dp.pdf A good tutorial]
|-
| 2016/07/28 ||Maoning Wang || Evolutionary learning ||Maoning Wang; ||[http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/%E6%96%87%E4%BB%B6:EA_%E7%8E%8B%E5%8D%AF%E5%AE%81.pdf]
|-
| 2016/07/29 ||Dong Wang || Reinforcement learning || Chao Xing ||
|-
| 2016/08/01 ||Dong Wang || Optimization || Caixia; || [http://cs229.stanford.edu/section/cs229-cvxopt.pdf Convex optimization I] [http://cs229.stanford.edu/section/cs229-cvxopt.pdf Convex optimization II]
|-
|}

文件:EA 王卯宁.pdf

2016-07-28T10:59:39Z

Wangmn：

Maoning Wang 2016-07-25

2016-07-24T17:57:55Z

Wangmn：以“last week: 1.read the book "Introduction to Evolutionary Computing" this week: 1.prepare the lecture on July 28th; 2.look for how/whether Evolutionary Computing...”为内容创建页面

last week:

1.read the book "Introduction to Evolutionary Computing"

this week:

1.prepare the lecture on July 28th;

2.look for how/whether Evolutionary Computing is used in econometrics recently.

2016-07-25

2016-07-24T17:50:49Z

Wangmn：以“Maoning Wang 2016-07-25”为内容创建页面

[[Maoning Wang 2016-07-25]]

2016 Summer Seminar for Machine learning

2016-07-22T07:40:32Z

Wangmn：

*Location: FIT-1-304

{| class="wikitable"
! Date !! Speaker!! Title !! Owner !! Materials
|-
| 2016/07/04 ||Dong Wang || Machine learning overview || || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt1.%20Overview%20of%20Machine%20Learning.pptx slides][http://arch.cslt.org/video/2016/sum-ML/lesson1-2.mp4 video(part 2)][http://cs229.stanford.edu/section/cs229-linalg.pdf Algebra review] [http://cs229.stanford.edu/section/cs229-prob.pdf probability review] [http://cs229.stanford.edu/section/gaussians.pdf Gaussian distribution][http://cs229.stanford.edu/notes/cs229-notes4.pdf Learning theory]
|-
| 2016/07/05 ||Dong Wang || Linear models || Aiting Liu Aodong Li|| [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt2.%20Linear%20Model.pptx slides][http://arch.cslt.org/video/2016/sum-ML/lesson2-1.mp4 video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson2-2.mp4 video(part 2)] [http://cs229.stanford.edu/notes/cs229-notes1.pdf NG's lecture 1] [http://cs229.stanford.edu/notes/cs229-notes2.pdf NG's lecture 2]
|-
| 2016/07/08 ||Dong Wang || Neural networks || || [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt3.%20Neural%20Network.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson3-1.mp4 video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson3-2.mp4 video(part 2)]
|-
| 2016/07/11 ||Dong Wang || Deep learning (1)|| Hang Luo Jiyuan Zhang [Zhiyuan]|| [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt4.%20Deep%20learning-1.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson4-1_Deep_learning.m4v video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson4-2_Deep_learning.m4v video(part 2)] [http://www.iro.umontreal.ca/~bengioy/talks/DL-Tutorial-NIPS2015.pdf NIPS 2015 tutorial]
|-
| 2016/07/12 ||Dong Wang || Deep learning (2)|| Hang Luo Jiyuan Zhang [Zhiyuan]|| [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt5.%20Deep%20learning-2.pptx slides] [http://arch.cslt.org/video/2016/sum-ML/lesson5-1_Deep_learning.m4v video(part 1)] [http://arch.cslt.org/video/2016/sum-ML/lesson5-2_Deep_learning.m4v video(part 2)] [http://www.icassp2016.org/SP16_PlenaryDeng_Slides.pdf Li Deng's ICASSP16 keynote]
|-
| 2016/07/13 ||Caixia Wang || Kernel methods || Caixia || [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/0/07/KM1.pdf slides] [http://arch.cslt.org/video/2016/sum-ML/lesson6_Kernel_method.m4v video] [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/b/b5/Kernel_Methods_for_Pattern_Analysis.pdf Kernel method book] [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/9/95/Pattern_Recognition_and_Machine_Learning.pdf pattern recognition 6-7]
|-
| 2016/07/18 ||Yang Feng || Graphical model (1) ||Jingyi Lin Ying Shi Yang Wang|| [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/d/dd/Chpt7._Graphical_models-bayesian_approach.pdf slides] [http://arch.cslt.org/video/2016/sum-ML/lesson7_Graphical_model.m4v video]
|-
| 2016/07/21 ||Dong Wang || Graphical model (2) ||Jingyi Lin Ying Shi Yang Wang||[http://wangd.cslt.org/talks/seminar/2016-sum-ml/chpt%208.%20Graphical%20models-2.pptx slides] [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chapt8/Graphical%20models-learningInference.pptx Yang's slides] [http://wangd.cslt.org/talks/seminar/2016-sum-ml/chapt8/GraphicalModel_Jordan.pdf Jordan's lecture]
|-
| 2016/07/25 ||Dong Wang || Unsupervised learning || ||
|-
| 2016/07/26 ||Dong Wang || Non parametric models ||Maoning Wang/ || [http://cs229.stanford.edu/section/cs229-gaussian_processes.pdf Gaussian process]
|-
| 2016/07/27 ||Dong Wang || Reinforcement learning || ||
|-
| 2016/07/28 ||Maoning Wang || Evolutionary learning ||Maoning Wang/ ||
|-
| 2016/07/29 ||Dong Wang || Optimization || || [http://cs229.stanford.edu/section/cs229-cvxopt.pdf Convex optimization I] [http://cs229.stanford.edu/section/cs229-cvxopt.pdf Convex optimization II]
|-
|}

Maoning Wang 2016-07-18

2016-07-17T18:36:25Z

Wangmn：

last week:

1.learn about the trading platform uqer/ricequant/quantopian

2.two possible ways to apply the method in "Robust Portfolio Optimization (NIPS 2015)":

(1)change "log-optimal portfolio strategy" to "Markowitz-type portfolio strategy in growth rate" [OV2007]

(2)extend the method on "covariance matrix" to that on "T_k, the autocovariance matrice"

3.learn about Evolutionary Computing via video lectures

this week:

1.read "Introduction to Evolutionary Computing"and prepare for the presentation on July 26.

Maoning Wang 2016-07-18

2016-07-17T18:35:50Z

Wangmn：以“last week: 1.learn about the trading platform uqer/ricequant/quantopian 2.two possible ways to apply the method in "Robust Portfolio Optimization (NIPS 2015)":...”为内容创建页面

last week:

1.learn about the trading platform uqer/ricequant/quantopian

2.two possible ways to apply the method in "Robust Portfolio Optimization (NIPS

2015)":

(1)change "log-optimal portfolio strategy" to "Markowitz-type portfolio

strategy in growth rate" [OV2007]

(2)extend the method on "covariance matrix" to that on "T_k, the autocovariance

matrice"

3.learn about Evolutionary Computing via video lectures

this week:

1.read "Introduction to Evolutionary Computing"and prepare for the presentation

on July 26.

2016-07-18

2016-07-17T18:34:48Z

Wangmn：

[[Mengyuan Zhao 2016-07-18]]

[[Ying Shi 2016-07-18]]

[[Zhiyong Zhang 2016-07-18]]

[[Maoning Wang 2016-07-18]]

Maoning Wang 2016-07-11

2016-07-11T01:31:16Z

Wangmn：以“last week: 1.test different 'k' (data shift value for self/cross-correlation) for PA algorithm this week: 1.theoretical analysis on PA algorithm: (1)the effect o...”为内容创建页面

last week:

1.test different 'k' (data shift value for self/cross-correlation) for PA algorithm

this week:

1.theoretical analysis on PA algorithm:

(1)the effect of "lsqlin"

(2)average: reduce the variance?

(3)1/x_t V.S. positive investment

2016-07-11

2016-07-11T01:21:11Z

Wangmn：

[[Yang Feng 2016-07-11]]

[[Mengyuan Zhao 2016-07-11]]

[[Lantian Li 2016-07-11]]

[[Ziwei Bai 2016-07-11]]

[[Caixia Wang 2016-07-11]]

[[Aodong Li 2016-07-11]]

[[Zhiyuan Tang 2016-07-11]]

[[jiyuan zhang 2016-07-11]]

[[Chao Xing 2016-07-11]]

[[Zhiyong Zhang 2016-07-11]]

[[Maoning Wang 2016-07-11]]

Maoning Wang 2016-07-04

2016-07-03T18:13:53Z

Wangmn：

last week:

1.analyze LiBin's PA algorithm:

(1) the solutions of PA in closed form correspond to [LM1990]

(2) a way to improve the algorithm may be to choose a suitable 'k' in eq3.4 in [LM1990] .

2.analyze LiBin's MA algorithm:

(1) derive the expression of E(\sum w_t x_t), where w_t,i=1/x_(t-1),i - 1/x_(t-1),m

(2) consider the relationship between MA algorithm and positive w in [LM1990]

3.data test: for constant 'k', the expected return varies as time goes on.

this week:

1.test the possible improvement of PA algorithm in OLPS_matlab

Maoning Wang 2016-07-04

2016-07-03T18:13:40Z

Wangmn：以“last week: 1.analyze LiBin's PA algorithm: (1) the solutions of PA in closed form correspond to [LM1990] (2) a way to improve the algorithm may be to choose a...”为内容创建页面

2016-07-04

2016-07-03T17:50:55Z

Wangmn：

[[Chao Xing 2016-07-04]]

[[Maoning Wang 2016-07-04]]

Maoning Wang 2016-06-27

2016-06-26T18:22:52Z

Wangmn：以“last week: 1.test how to find 'k' in equation(3.4) in [LoMacKinlay1990] [http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/%E6%96%87%E4%BB%B6:Tk.bmp] 2.paramete...”为内容创建页面

last week:

1.test how to find 'k' in equation(3.4) in [LoMacKinlay1990] [http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/%E6%96%87%E4%BB%B6:Tk.bmp]

2.parameter estimation of the assumptions in LiBin's articles

3.learn to use ntstool in matlab to predict the next price

this week:

1.test other datasets on 'k' in equation(3.4)

2.find the reason why OLMAR works better than PAMR. My tools:(1)theoretical analysis on Perceptron (2) the relationship between the form of PAMR/OLMAR and equation(3.3) in [LoMacKinlay1990]

文件:Tk.bmp

2016-06-26T18:07:54Z

Wangmn：

2016-06-27

2016-06-26T18:02:47Z

Wangmn：

[[Zhiyuan Tang 2016-06-27]]

[[Mengyuan Zhao 2016-06-27]]

[[Maoning Wang 2016-06-27]]

Maoning Wang 2016-06-20

2016-06-20T00:59:45Z

Wangmn：以“last week: 1.learn about Stochastic Difference Equation; consider how to model with SDE in the scenario of online portfolio selection this week: 1.read the paper...”为内容创建页面

last week:

1.learn about Stochastic Difference Equation; consider how to model with SDE in the scenario of online portfolio selection

this week:

1.read the paper "where are contrarian profits due to stock market overreaction"

2.parameter estimation of the revised model used by LiBin/SDE

3.upper/lower bound of Passive Aggressive Mean Reversion algorithm

2016-06-19

2016-06-20T00:59:21Z

Wangmn：

[[Mengyuan Zhao 2016-06-19]]

[[Chao Xing 2016-06-20]]

[[Zhiyuan Tang 2016-06-20]]

[[Zhiyong Zhang 2016-06-20]]

[[Lantian Li 2016-06-20]]

[[Qixin Wang 2016-06-20]]

[[Maoning Wang 2016-06-20]]

Finance-weekly-reading

2016-06-17T01:45:52Z

Wangmn：

{|class='wikitable'
!Time !! Title !! Leader !! Paper
|-
|2015-08-03 ||Detect & Describe: Deep learning of bank stress in the news || Dong Wang || Samuel Ronnqvist, Peter Sarlin, arxiv 2015.07.25 [[http://arxiv.org/pdf/1507.07870.pdf pdf]]
|-
|2015-08-05 ||Analysis of Hidden Markov Models and Support vector machines in financial applications|| Rong Liu || Satish Rao Jerry Hong 2010[[http://www.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-63.pdf pdf]]
|-
|2015-08-10 ||NEURAL NETWORKS APPLIED TO STOCK MARKET FORECASTING: AN EMPIRICAL ANALYSIS|| Yang Yu || Leandro Maciel, Rosangela Ballini 2010 [[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/7/76/NEURAL_NETWORKS_APPLIED_TO_STOCK_MARKET_FORECASTING_AN_EMPIRICAL_ANALYSIS.pdf pdf]]
|-
|2015-08-12 ||A Decision Tree-Rough Set Hybrid System for Stock Market Trend Prediction|| Rong Liu || 2010 [[http://www.ijcaonline.org/volume6/number9/pxc3871449.pdf pdf]]
|-
|2015-08-12 ||A Comparision of Three Network Portfolio Selection Methods || Dong Wang || 2015 [[http://arxiv.org/pdf/1512.01905.pdf pdf]]
|-
|2016-06-12 ||Online Portfolio Selection: A Survey || Maoning Wang|| January 2014 [[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/9/9b/A35-li.pdf]]
|-
|2016-06-12 ||SEQUENTIALMONTECARLOSAMPLINGFORCORRELATEDLATENTLONG-MEMORY TIME-SERIE || Maoning Wang|| ICASSP 2016 [[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/b/b4/0006580.pdf]]
|-
|2016-06-17 ||PAMR: Passive Aggressive Mean Reversion Strategy for Portfolio Selection|| Author: LiBin|| Machine Learning May 2012, Volume 87, Issue 2, pp 221-258 [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.421.579&rep=rep1&type=pdf]]
|-
|2016-06-17 ||随机微分方程简介|| Author: 陈堰平|| ppt 2008年12月14日 [[http://cos.name/wp-content/uploads/2008/12/stochastic-differential-equation-with-r.pdf]]
|-
|}

文件:截图 pdf.pdf

2016-06-13T02:35:05Z

Wangmn：1.道琼斯 2.恒生 3.法国cac 4.英国ft

1.道琼斯
2.恒生
3.法国cac
4.英国ft

Maoning Wang 2016-06-13

2016-06-13T02:10:36Z

Wangmn：

last week

1. I cannot find (economic or mathematical) theoretical description of LiBin's concrete mean reversion assumption. It seems hard to do hypothesis testing.

2. The paper 'SEQUENTIAL MONTECARLO SAMPLING FOR CORRELATED LATENTLONG-MEMORY TIME-SERIES' (ICASSP 2016) studies how to generate random data following models like dS/S=udt+cdx, where dx follows normal distribution.

this week

1.understand how to change the differential equation dS/S=udt+cdx into the example mentioned in the ICASSP 2016 paper.

2.understand how LiBin changes a learning algorithm into an OLP algorithm.

Maoning Wang 2016-06-13

2016-06-13T02:09:51Z

Wangmn：以“last week 1. I cannot find (economic or mathematical) theoretical description of LiBin's concrete mean reversion assumption. It seems hard to do hypothesis testing....”为内容创建页面

last week
1. I cannot find (economic or mathematical) theoretical description of LiBin's concrete mean reversion assumption. It seems hard to do hypothesis testing.

2. The paper 'SEQUENTIAL MONTECARLO SAMPLING FOR CORRELATED LATENTLONG-MEMORY TIME-SERIES' (ICASSP 2016) studies how to generate random data following models like dS/S=udt+cdx, where dx follows normal distribution.

this week
1.understand how to change the differential equation dS/S=udt+cdx into the example mentioned in the ICASSP 2016 paper.

2.understand how LiBin changes a learning algorithm into an OLP algorithm.

2016-06-13

2016-06-13T01:53:57Z

Wangmn：

[[Mengyuan Zhao 2016-06-13]]

[[Zhiyuan Tang 2016-06-13]]

[[Lantian Li 2016-06-13]]

[[Qixin Wang 2016-06-13]]

[[Jiyuan Zhang 2016-06-13]]

[[Chao Xing 2016-06-13]]

[[Maoning Wang 2016-06-13]]

Finance-weekly-reading

2016-06-12T02:20:35Z

Wangmn：

文件:0006580.pdf

2016-06-12T02:17:15Z

Wangmn：SEQUENTIALMONTECARLOSAMPLINGFORCORRELATEDLATENTLONG-MEMORY TIME-SERIES

SEQUENTIALMONTECARLOSAMPLINGFORCORRELATEDLATENTLONG-MEMORY TIME-SERIES

Finance-weekly-reading

2016-06-12T02:13:34Z

Wangmn：

文件:A35-li.pdf

2016-06-12T02:05:40Z

Wangmn：LiBin的一系列online portfolio selection算法的综述

LiBin的一系列online portfolio selection算法的综述

Maoning Wang 2016-06-06

2016-06-05T19:13:19Z

Wangmn：以“last week: 1.test the lib OLPS on different datasets: stock data in recent 3 years of the USA(daoqiongsi), China(ss, sz), HK, UK, Fr. 2.find a marketplace for fina...”为内容创建页面

last week:

1.test the lib OLPS on different datasets: stock data in recent 3 years of the USA(daoqiongsi), China(ss, sz), HK, UK, Fr.

2.find a marketplace for financial and economic data: https://www.quandl.com/. Quandl has MATLAB API. There is free data of Futures Exchanges and so on.

this week:

1.mean reversion property? multiple-period mean reversion? [LiBin2012] Seemingly unrelated regression (SUR) test?[Chaudhuri] A quick survey in Chinese:[Gaoli].

2.(if I have time)for futures' model? How to transform LiBin's models into models for futures contracts? maybe the relative price is not quite suitable...

2016-06-06

2016-06-05T18:23:23Z

Wangmn：

[[Qixin Wang 2016-06-06]]

[[Maoning Wang 2016-06-06]]

Maoning Wang 2016-05-30

2016-05-29T18:14:28Z

Wangmn：以“last week: 1. understanding Kelly Criterion: most online portfolio selection algorithms introduced by LiBin can be interpreted as applications of Kelly Criterion un...”为内容创建页面

last week:

1. understanding Kelly Criterion: most online portfolio selection algorithms introduced by LiBin can be interpreted as applications of Kelly Criterion under reletively simple assumptions.

2.understanding basic perception algorithms: but finally I find LiBin only uses the concept of loss function like \vec{x}\dot\vec{b}-\epsilon...

3.test the lib OLPS:

(1)the best algorithm may be "Online Moving Average Reversion" as shown by their data

(2)I encounter problems of function 'fetch' in Matlab!

this week:

1.test OLPS with data selected by ourselves

2.consider why "Online Moving Average Reversion" performs better

2016-05-30

2016-05-29T17:58:02Z

Wangmn：

[[Mengyuan Zhao 2016-05-30]]

[[Maoning Wang 2016-05-30]]

Maoning Wang 2016-05-23

2016-05-22T17:41:30Z

Wangmn：以“last week: 1.literature review on recent problems in the field of computational finance 2.decide to choose "online portfolio selection" as our starting point 3.re...”为内容创建页面

last week:

1.literature review on recent problems in the field of computational finance

2.decide to choose "online portfolio selection" as our starting point

3.read(skim) the survey written by Li Bin

4.give a report on "Markowitz's key contribution to portfolio theory"

this week:

1.try to understand "Kelly criterion", particularly the way that researchers change such an model into algorithms

2.find:

(1) if there is an improved such model with risk/volatility/variance involved; turn it into an algorithm if there is one such model; or, turn the baseline algorithms into those considering risk……

(2) if there are any methods to give predictions that Kelly criterion can hence apply; or consider "interval estimation" instead of "point estimation" in the prediction methods

2016-05-23

2016-05-22T17:21:28Z

Wangmn：

[[Lantian Li 2016-05-23]]

[[Maoning Wang 2016-05-23]]

文件:汇报资产组合中几个评价指标的解释.pdf

2016-05-20T06:00:38Z

Wangmn：

Maoning Wang 2016-05-16

2016-05-16T12:37:09Z

Wangmn：

last week:

1.learn basic concepts on financial engineering via Video Training Courses

this week:

1.literature review on recent computational finance research(3 days)

2.intensive reading in the field that we choose (4 days)

Maoning Wang 2016-05-16

2016-05-16T12:36:37Z

Wangmn：以“last week: 1.learn basic concepts on financial engineering via Video Training Courses this week: 1.literature review on recent computational finance research(3 days...”为内容创建页面

last week:
1.learn basic concepts on financial engineering via Video Training Courses

this week:
1.literature review on recent computational finance research(3 days)
2.intensive reading in the field that we choose (4 days)

cslt Wiki - 用户贡献 [zh-cn]

FIN Status Report 2016-09-05

FIN Status Report 2016-08-29

FIN Status Report 2016-08-22

FIN Status Report 2016-08-22

FIN Status Report 2016-08-22

2016-08-22

Maoning Wang 2016-08-15

2016-08-15

2016-08-08 Maoning Wang

2016-08-08

Maoning Wang 2016-08-01

2016-08-01

2016 Summer Seminar for Machine learning

文件:Introduction to Evolutionary Computing.pdf

2016 Summer Seminar for Machine learning

文件:EA 王卯宁.pdf

Maoning Wang 2016-07-25

2016-07-25

2016 Summer Seminar for Machine learning

Maoning Wang 2016-07-18

Maoning Wang 2016-07-18

2016-07-18

Maoning Wang 2016-07-11

2016-07-11

Maoning Wang 2016-07-04

Maoning Wang 2016-07-04

2016-07-04

Maoning Wang 2016-06-27

文件:Tk.bmp

2016-06-27

Maoning Wang 2016-06-20

2016-06-19

Finance-weekly-reading

文件:截图 pdf.pdf

Maoning Wang 2016-06-13

Maoning Wang 2016-06-13

2016-06-13

Finance-weekly-reading

文件:0006580.pdf

Finance-weekly-reading

文件:A35-li.pdf

Maoning Wang 2016-06-06

2016-06-06

Maoning Wang 2016-05-30

2016-05-30

Maoning Wang 2016-05-23

2016-05-23

文件:汇报 资产组合中几个评价指标的解释.pdf

Maoning Wang 2016-05-16

Maoning Wang 2016-05-16

文件:汇报资产组合中几个评价指标的解释.pdf