We sincerely thank Reviewer 1 for referring us to four relevant papers [1-4]. Paper [1] provides a very interesting relationship between Fisher divergence and Stein's operator, whereby Hyvarinen Paper [4] establishes a more general result than that of S. If the model class is well-specified then convergence to the data generating distribution is guaranteed. Fano's inequality also gives lower bounds of model selection/message decoding error (so larger'A kernelized Stein discrepancy for goodness-of-fit tests.' We appreciate Reviewer 2's comments and recommendations. We will do another proof reading and remove typos.

ICML is the leading international machine learning conference and is supported by the International Machine Learning Society (IMLS). The talks for this event are currently behind a password firewall temporarily for QA.

The International Conference on Machine Learning (ICML) is the leading international academic conference in machine learning, attracting 2000 participants. This year it was held in NYC and I attended on behalf of Init.ai. Three of the tutorial sessions I attended were quite impactful. Anyone working on conversational apps, chatbots, and deep learning would be interested in these topics. I've written before about Residual Neural Network research, but listening to Kaiming was informative.

The International Conference on Machine Learning (ICML) is the leading international academic conference in machine learning, attracting 2000 participants. This year it was held in NYC and I attended on behalf of Init.ai. Three of the tutorial sessions I attended were quite impactful. Anyone working on conversational apps, chatbots, and deep learning would be interested in these topics. I've written before about Residual Neural Network research, but listening to Kaiming was informative.

Personally, I am into deep learning, esp. However, for the interest of also other reddit-readers here, I think also a listing of any ML-papers that have a very high novelty factor could be interesting.

At ICML last year and the year before the amount of capacity that needed to fit everyone on any single day was about 1500. My advice was to expect 2000 and have capacity for 2500 because "New York" and "Machine Learning". I was not involved in the venue negotiations, but my understanding is that they were difficult, with liabilities over 1M for IMLS the nonprofit which oversees ICML year to year. The result was a conference plan with a maximum capacity of 1800 for the main conference, a bit less for workshops, and perhaps 1000 for tutorials. Then the NIPS registration numbers came in: 3900 last winter.

ICML 2016 is in New York City. I expect it to be the largest ICML by far given the destination--New York is the place which is perhaps easiest to reach from anywhere in the world and it has the largest machine learning meetup anywhere in the world. I am the general chair this year, which is light in work but heavy in responsibilities. Markus Weimer also points out the 2016 KDD Cup which has a submission deadline of December 6. KDD Cup datasets have become common reference for many machine learning papers, so this is a good way to get your problem solved well by many people.