paper "Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data" with BertrandThirion and Gael Varoquaux got accepted to #ICML2019 ! Arxiv: https://arxiv.org/abs/1807.11718# code: https://github.com/sergulaydore/Feature-Grouping-Regularizer

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Deep reinforcement learning algorithms are impressive, but only when they work. In reality, they are largely unreliable and can yield very different results. larocheromain proposes two ways to achieve reliability in RL: https://aka.ms/AA5ann9 #ICML2019

Erin LeDell: Happy to share our #ICML2019 #AutoML Workshop paper, "An Open Source AutoML Benchmark". We present a new #opensource AutoML benchmarking system and include results on: H2O AutoML, auto-sklearn, TPOT, Auto-WEKA
📰 Paper: https://www.automl.org/wp-content/uploads/2019/06/automlws2019_Paper45.pdf
👩‍💻 Code: https://github.com/openml/automlbenchmark/

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Vithursan Thangarasa
Excited to be presenting my work on "Differentiable Hebbian Plasticity for Continual Learning"
(https://openreview.net/forum?id=r1x-E5Ss34 )
at the #ICML2019 Adaptive and Multi-task Learning workshop. Blog post to my
paper: https://vithursant.com/dhp-softmax/ .

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#ICML2019 live from Long Beach, CA, via icmlconf Learn more
→ https://mld.ai/icml2019-live #machinelearning #ML #mldcmu #ICML

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deep learning for breast cancer screening at the AI for Social Good Workshop at #ICML2019

Paper: https://arxiv.org/abs/1903.08297
Code: https://github.com/nyukat/breast_cancer_classifier

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Notes from Thirty-sixth International Conference on Machine Learning here:
https://david-abel.github.io/notes/icml_2019.pdf
#ICML2019

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Best paper award at #ICML2019 main idea: unsupervised learning of disentangled representations is fundamentally impossible without inductive biases. Verified theoretically & experimentally.
https://arxiv.org/pdf/1811.12359.pdf

✴️ @AI_Python_EN

I'll be sharing 5 Lessons Learned Helping 200,000 non-ML experts* use ML as an #ICML2019 AutoML workshop keynote
https://sites.google.com/view/automl2019icml/schedule?authuser=0

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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations"
http://goo.gle/2IyFqTO
recipient of an #ICML2019 Best Paper Award! Learn more in the blog post at
http://goo.gle/2KaMs48 .

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Best Papers Awards #ICML2019
The Thirty-sixth International Conference on Machine Learning, Long Beach, USA

(1)Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations https://arxiv.org/pdf/1811.12359.pdf
Congratulations to the GoogleAI team of Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly,Bernhard Schölkopf, Olivier Bachem

(2)Rates of Convergence for Sparse Variational Gaussian Process Regression
https://arxiv.org/pdf/1903.03571.pdf
Kudos to David R. Burt, Carl E. Rasmussen, Mark van der Wilk of University of Cambridge

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