# Difference between revisions of "sign up for your presentation"

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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). | 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 == | ||

{| class="wikitable" | {| class="wikitable" | ||

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|width="200pt"|Presentation (2) | |width="200pt"|Presentation (2) | ||

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+ | |Nov 16|| Sepideh Seifzadeh [http://www.cs.berkeley.edu/~jordan/papers/lacoste-sha-jordan-nips08.pdf] [[DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification|Summary]] || Manda Winlaw [http://www-stat.stanford.edu/~tibs/Correlate/pmd.pdf], [[A Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis|Summary]] | ||

+ | |- | ||

+ | |- | ||

+ | |Nov 18||Rahul [http://dsp.rice.edu/files/cs/baraniukCSlecture07.pdf][[Compressive Sensing| Summary]]||Fatemeh Dorri [http://www.cs.berkeley.edu/~jordan/papers/daspremont-siam-review.pdf],[[A Direct Formulation For Sparse PCA Using Semidefinite Programming|Summary]] | ||

+ | |- | ||

+ | |- | ||

+ | |Nov 23 || Pouria Fewzee ||Lisha Yu[http://books.nips.cc/papers/files/nips21/NIPS2008_0197.pdf][[Deflation_Methods_for_Sparse_PCA|Summary]] | ||

+ | |- | ||

+ | |- | ||

+ | |Nov 25 ||Laleh Ghoraie [http://arxiv.org/abs/0903.3131][[Matrix_Completion_with_Noise| Summary]] || Mehrdad Gangeh [http://pami.uwaterloo.ca/~mgangeh/SupervisedDictionaryLearning.pdf][[Supervised Dictionary Learning|Summary]] | ||

+ | |- | ||

+ | |- | ||

+ | |Nov 30 || Greg D'Cunha [http://www.stanford.edu/~hllee/icml07-selftaughtlearning.pdf] [[Self-Taught Learning| Summary]]|| Mohammad Derakhshani [http://faculty.washington.edu/mfazel/nucnorm_acc_final.pdf], | ||

+ | [[A Rank Minimization Heuristic with Application to Minimum Order System Approximation|Summary]] | ||

+ | |- | ||

+ | |Dec 2 || Yongpeng Sun [http://www.google.ca/url?sa=t&source=web&cd=1&ved=0CBUQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.76.5871%26rep%3Drep1%26type%3Dpdf&rct=j&q=%22Uncovering%20shared%20structures%20in%20multiclass%20classification.%22&ei=5We3TJTVOYH98Abi3czpCQ&usg=AFQjCNF6MSD0BNolGNe4z6d1RKeR7ZWJsw&sig2=B9YHZC8V9q6nIPa4vJKr-g&cad=rja] [http://www.wikicoursenote.com/wiki/Uncovering_Shared_Structures_in_Multiclass_Classification Summary] || Ryan Case [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.92.4570&rep=rep1&type=pdf] [[Compressive_Sensing_(Candes)|Summary]] | ||

+ | |- | ||

+ | |- | ||

+ | |} | ||

+ | |} | ||

+ | |||

+ | == Set B == | ||

+ | |||

+ | |||

+ | {| class="wikitable" | ||

+ | |||

+ | {| border="1" cellpadding="2" | ||

+ | |- | ||

+ | |width="100pt"|Name | ||

+ | |width="900pt"|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 [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]] | ||

+ | |- | ||

+ | |Fatemeh Dorri||Optimal Solutions forSparse Principal Component Analysis[http://www.princeton.edu/~aspremon/OptSPCA.pdf] [[ Optimal Solutions forSparse Principal Component Analysis| Summary]] | ||

+ | |- | ||

+ | |Lisha Yu|| Maximum‐margin matrix factorization[http://ttic.uchicago.edu/~nati/Publications/MMMFnips04.pdf] | ||

+ | |- | ||

+ | |Ryan Case|| Probabilistic non-linear principal component analysis with Gaussian process latent variable models [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.66.8581&rep=rep1&type=pdf] [[Probabilistic_PCA_with_GPLVM | Summary]] | ||

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− | |||

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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] | ||

|- | |- | ||

− | | | + | |Manda Winlaw|| Consistency of trace norm minimization [http://www.di.ens.fr/~fbach/bach08a.pdf] [[Consistency of Trace Norm Minimization| Summary]] |

|- | |- | ||

+ | |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 |