links

_doc/sphinxdoc/source/blog/2017/2017-09-13_papers.rst

@@ -0,0 +1,24 @@
+
+.. blogpost::
+    :title: Quelques papiers que j'aimerais avoir le temps de lire
+    :keywords: articles
+    :date: 2017-09-13
+    :categories: biblio
+
+    * `Two New Approaches to Compressed Sensing Exhibiting Both Robust Sparse Recovery and the Grouping Effect <http://www.jmlr.org/papers/volume18/14-453/14-453.pdf>`_
+    * `On the Consistency of Ordinal Regression Methods <http://www.jmlr.org/papers/volume18/15-495/15-495.pdf>`_
+    * `Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis <http://www.jmlr.org/papers/volume18/16-061/16-061.pdf>`_
+    * `Joint Label Inference in Networks <http://www.jmlr.org/papers/volume18/16-214/16-214.pdf>`_
+    * `Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA <http://www.jmlr.org/papers/volume18/16-526/16-526.pdf>`_
+    * `Fundamental Conditions for Low-CP-Rank Tensor Completion <http://www.jmlr.org/papers/volume18/17-189/17-189.pdf>`_
+    * `Averaged Collapsed Variational Bayes Inference <http://www.jmlr.org/papers/volume18/14-249/14-249.pdf>`_
+    * `Communication-efficient Sparse Regression <http://www.jmlr.org/papers/volume18/16-002/16-002.pdf>`_
+    * `Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks <http://www.jmlr.org/papers/volume18/16-270/16-270.pdf>`_
+    * `Automatic Differentiation Variational Inference <http://www.jmlr.org/papers/volume18/16-107/16-107.pdf>`_
+    * `A Unified Formulation and Fast Accelerated Proximal Gradient Method for Classification <http://www.jmlr.org/papers/volume18/16-274/16-274.pdf>`_
+    * `Breaking the Curse of Dimensionality with Convex Neural Networks <http://www.jmlr.org/papers/volume18/14-546/14-546.pdf>`_
+    * `Preference-based Teaching <http://www.jmlr.org/papers/volume18/16-460/16-460.pdf>`_
+    * `Online Bayesian Passive-Aggressive Learning∗ <http://www.jmlr.org/papers/volume18/14-188/14-188.pdf>`_
+    * `Learning Local Dependence In Ordered Data <http://www.jmlr.org/papers/volume18/16-198/16-198.pdf>`_
+    * `Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers <http://www.jmlr.org/papers/volume18/15-240/15-240.pdf>`_
+    * `Clustering from General Pairwise Observations with Applications to Time-varying Graphs∗ <http://www.jmlr.org/papers/volume18/15-659/15-659.pdf>`_

_doc/sphinxdoc/source/td_2a_mlbasic.rst

@@ -170,6 +170,7 @@ Clustering
 * `Scalable Density-Based Clustering with Quality Guarantees using Random Projections <http://alumni.cs.ucr.edu/~mvlachos/erc/projects/density-based/paper.pdf>`_
 * `Clustering Via Decision Tree Construction <http://web.cs.ucla.edu/~wwc/course/cs245a/CLTrees.pdf>`_
   (implémentation en python `dimitrs/CLTree <https://github.com/dimitrs/CLTree>`_)
+* `Spectral Clustering Based on Local PCA <http://www.jmlr.org/papers/volume18/14-318/14-318.pdf>`_
 
 *Modules*
 
@@ -467,6 +468,7 @@ Modèles bayésiens
 * `PyMC3 <https://pymc-devs.github.io/pymc3/notebooks/getting_started.html>`_
 * `bayespy <http://bayespy.org/en/latest/>`_
 * `kabuki <https://pypi.python.org/pypi/kabuki/>`_
+* `bnpy <https://github.com/bnpy/bnpy>`_
 
 Factorization Machines
 ++++++++++++++++++++++

_doc/sphinxdoc/source/td_2a_mlplus.rst

@@ -309,6 +309,7 @@ Anonymisation des données
 * `Preserving Privacy of Continuous High-dimensional Data with Minimax Filters <http://www.jmlr.org/proceedings/papers/v38/hamm15.pdf>`_
 * `Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo <http://www.jmlr.org/proceedings/papers/v37/wangg15.pdf>`_
 * `Privatics (INRIA) <https://team.inria.fr/privatics/>`_
+* `Differential Privacy for Bayesian Inference through Posterior Sampling∗ <http://www.jmlr.org/papers/volume18/15-257/15-257.pdf>`_
 
 *Algorithmes*
 
@@ -457,6 +458,7 @@ exploitable par les algorithmes d'apprentissage.
   `corenlpy <https://github.com/enewe101/corenlpy>`_
 * `lda2vec <https://github.com/cemoody/lda2vec>`_
 * `glove-python <https://github.com/maciejkula/glove-python>`_
+* `tethne <http://diging.github.io/tethne/>`_
 
 *Modules moins ML*