Top Tweets for #StatLearn19
#StatLearn18 #StatLearn19 .... see you next year for #StatLearn20 ? Hopefully ! And Thank you @TabMoLabs for the opportunity 🙏🏻♥️
Guess who is speaking about #optimalTransport for #MachineLearning at #statlearn19! @grenobledata @StatFr @inria_grenoble @u_grenoblealpes
E. Hüllermeier concludes this Nice 10th edition of #statlearn19 with a talk about learning rankings! @grenobledata @inria_grenoble @u_grenoblealpes @StatFr
Guess who is speaking about #optimalTransport for #MachineLearning at #statlearn19! @grenobledata @StatFr @inria_grenoble @u_grenoblealpes
@GaelVaroquaux proposed similarity encoding that is a soft version of one hot encoding for categorical variables stored as text. Based on string similarity. Paper in #arXiv https://t.co/7CgU7usMwz #statlearn19
@GaelVaroquaux starts his talk at #statlearn19 with this quote about the importance of data preparation in #DataScience
In Data Science, 80% of time spent prepare data, 20% of time spent complain about need for prepare data.
.@GaelVaroquaux talks now at #statlearn19 about Statistical Leraning on #DirtyData! @grenobledata @Inria @u_grenoblealpes @scikit_learn
Max Welling opens the 2nd day of #statlearn19 with a talk about Gauge Fields in #DeepLearning! @StatFr @grenobledata @u_grenoblealpes @inria_grenoble
#statlearn19 on the top!
Julie Josse on consistency of supervised learning with missing values #statlearn19
@JulieJosseStat about imputing missing data using a joint model. Many solutions and packages including here #rstats package missMDA https://t.co/tCYf774FSN, also a recent approach of @pamattei based on autoencoder called MIWAE https://t.co/bVHVQ4yszt #statlearn19
@cbouveyron @grenobledata @u_grenoblealpes @inria_grenoble @StatFr Now @JulieJosseStat talks about Machine Learning with missing values : a very practical issue ! #statlearn #statlearn19
Judith Rousseau talks about theoretical aspects of deep neural network training at #statlearn19. Very inspiring! @grenobledata @u_grenoblealpes @inria_grenoble @StatFr
Thank you for an interesting talk! #StatLearn19
Clustering ad campaigns to predict their click through rate (and other metrics). Interestingly, they're not yet looking at the content of the ads, but rather at "external" features like size, category, device etc. and it's working pretty well #statlearn19
Clustering ad campaigns to predict their click through rate (and other metrics). Interestingly, they're not yet looking at the content of the ads, but rather at "external" features like size, category, device etc. and it's working pretty well #statlearn19
Mixed data classification, clustering and co-clustering by @mversionpenny and colleagues. R package available, python hopefully in a couple of months #statlearn19
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