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|Greg D'Cunha || Probabilistic matrix factorization [http://www.google.ca/url?sa=t&source=web&cd=1&ved=0CBkQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.127.6198%26rep%3Drep1%26type%3Dpdf&rct=j&q=Probabilistic%20matrix%20factorization&ei=Crm9TPDpDIiosAOHhqmYDQ&usg=AFQjCNHdgX8UXD5fthc85O4lJxH-bRD86Q&sig2=OgSnMU_ax6PT0XeY3UGS9A&cad=rja] [[Probabilistic Matrix Factorization | Summary]] | |Greg D'Cunha || Probabilistic matrix factorization [http://www.google.ca/url?sa=t&source=web&cd=1&ved=0CBkQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.127.6198%26rep%3Drep1%26type%3Dpdf&rct=j&q=Probabilistic%20matrix%20factorization&ei=Crm9TPDpDIiosAOHhqmYDQ&usg=AFQjCNHdgX8UXD5fthc85O4lJxH-bRD86Q&sig2=OgSnMU_ax6PT0XeY3UGS9A&cad=rja] [[Probabilistic Matrix Factorization | Summary]] | ||
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|Fatemeh Dorri||Optimal Solutions forSparse Principal Component Analysis[http://www.princeton.edu/~aspremon/OptSPCA.pdf] | |Fatemeh Dorri||Optimal Solutions forSparse Principal Component Analysis[http://www.princeton.edu/~aspremon/OptSPCA.pdf] [[ Optimal Solutions forSparse Principal Component Analysis| Summary]] | ||
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|Lisha Yu|| Maximum‐margin matrix factorization[http://ttic.uchicago.edu/~nati/Publications/MMMFnips04.pdf] | |Lisha Yu|| Maximum‐margin matrix factorization[http://ttic.uchicago.edu/~nati/Publications/MMMFnips04.pdf] | ||
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|Yongpeng Sun|| Multi‐Task Feature Learning [http://books.nips.cc/papers/files/nips19/NIPS2006_0251.pdf] [http://www.wikicoursenote.com/wiki/Multi-Task_Feature_Learning Summary] | |Yongpeng Sun|| Multi‐Task Feature Learning [http://books.nips.cc/papers/files/nips19/NIPS2006_0251.pdf] [http://www.wikicoursenote.com/wiki/Multi-Task_Feature_Learning Summary] | ||
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|Manda Winlaw|| Consistency of trace norm minimization [http://www.di.ens.fr/~fbach/bach08a.pdf] | |Manda Winlaw|| Consistency of trace norm minimization [http://www.di.ens.fr/~fbach/bach08a.pdf] [[Consistency of Trace Norm Minimization| Summary]] | ||
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|Laleh Ghoraie|| A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization [http://jmlr.csail.mit.edu/papers/v10/abernethy09a.html][[ A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization | Summary]] | |||
Latest revision as of 08:45, 30 August 2017
Sign up for your presentation in the following table. Put your name and a link to the paper that you are going to present. Chose a date between Nov 16 and Dec 2 (inclusive).
Set A
| Date | Presentation (1) | Presentation (2) |
| Nov 16 | Sepideh Seifzadeh [1] Summary | Manda Winlaw [2], Summary |
| Nov 18 | Rahul [3] Summary | Fatemeh Dorri [4],Summary |
| Nov 23 | Pouria Fewzee | Lisha Yu[5]Summary |
| Nov 25 | Laleh Ghoraie [6] Summary | Mehrdad Gangeh [7]Summary |
| Nov 30 | Greg D'Cunha [8] Summary | Mohammad Derakhshani [9], |
| Dec 2 | Yongpeng Sun [10] Summary | Ryan Case [11] Summary |
Set B
| Name | Second paper (The paper that you are going to write a critic on it. This is different from the paper that you have chosen for presentation.) |
| Greg D'Cunha | Probabilistic matrix factorization [12] Summary |
| Fatemeh Dorri | Optimal Solutions forSparse Principal Component Analysis[13] Summary |
| Lisha Yu | Maximum‐margin matrix factorization[14] |
| Ryan Case | Probabilistic non-linear principal component analysis with Gaussian process latent variable models [15] Summary |
| Yongpeng Sun | Multi‐Task Feature Learning [16] Summary |
| Manda Winlaw | Consistency of trace norm minimization [17] Summary |
| Laleh Ghoraie | A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization [18] Summary |