DataScience4you2me

I spent the last month becoming a connoisseur of digital contact tracing approaches since this seems like something where I might be able to help. Many other people have been thinking along similar lines (great), but I also see several misconceptions that even smart and deeply involved people are making. For the following a key distinction to understand is between proximity and location approaches. In proximity approaches (such as DP3T , TCN , MIT PACT(*) , Apple or one of the UW PACT(*) protocols which I am involved in) smartphones use Bluetooth low energy and possibly ultrasonics to discover other smartphones nearby. Location approaches (such as MIT Safe Paths or Israel ) instead record the absolute location of the device based on gps, cell tower triangulation, or wifi signals. Location traces are both poor quality and intrinsically identifying Many people associate the ability of a phone to determine where it is with the ability to discover where it is with high precision. Th...

ICML , ICLR , and NeurIPS are all considering or experimenting with code and data submission as a part of the reviewer or publication process with the hypothesis that it aids reproducibility of results. Reproducibility has been a rising concern with discussions in paper , workshop , and invited talk . The fundamental driver is of course lack of reproducibility. Lack of reproducibility is an inherently serious and valid concern for any kind of publishing process where people rely on prior work to compare with and do new things. Lack of reproducibility (due to random initialization for example) was one of the things leading to a period of unpopularity for neural networks when I was a graduate student. That has proved nonviable (Surprise! Learning circuits is important!), but the reproducibility issue remains. Furthermore, there is always an opportunity and latent suspicion that authors ‘cheat’ in reporting results which could be allayed using a reproducible approach. With the above sa...

ICML 2019 has an option for supplementary code submission that the authors can use to provide additional evidence to bolster their experimental results. Since we have been getting a lot of questions about it, here is a Frequently Asked Questions for authors. 1. Is code submission mandatory? No. Code submission is completely optional, and we anticipate that high quality papers whose results are judged by our reviewers to be credible will be accepted to ICML, even if code is not submitted. 2. Does submitted code need to be anonymized? ICML is a double blind conference, and we expect authors to put in reasonable effort to anonymize the submitted code and institution. This means that author names and licenses that reveal the organization of the authors should be removed. Please note that submitted code will not be made public — eg, only the reviewers, Area Chair and Senior Area Chair in charge will have access to it during the review period. If the paper gets accepted, we expect the a...

The ICML 2019 Conference will be held from June 10-15 in Long Beach, CA — about a month earlier than last year. To encourage reproducibility as well as high quality submissions, this year we have three major changes in place. There is an abstract submission deadline on Jan 18, 2019. Only submissions with proper abstracts will be allowed to submit a full paper, and placeholder abstracts will be removed. The full paper submission deadline is Jan 23, 2019. This year, the author list at the paper submission deadline (Jan 23) is final. No changes will be permitted after this date for accepted papers. Finally, to foster reproducibility, we highly encourage code submission with papers. Our submission form will have space for two optional supplementary files — a regular supplementary manuscript, and code. Reproducibility of results and easy accessibility of code will be taken into account in the decision-making process. Our full Call for Papers is available  here. Kamalika Chaudh...

This is not at all related to Machine Learning. I lived in Squirrel Hill as a graduate student at Carnegie Mellon so the massacre there is feeling particularly immediate. While the person who did it is obviously culpable, the pattern of events makes it clear that others bear responsibility as well. This pattern includes an attempted bomber of Democrats and Trump critics by a Trump fanboy . It also includes a more general cross section of Republicans and their leaders pushing anti-semitism and more general xenophobia about migrants . I don’t believe that stochastic terrorism is the goal here. Instead, I have a rather pessimal view of politics in which politicians do pretty much anything to get re-elected, at least in aggregate. Donald Trump’s presidential campaign showed how to do this with a platform of populism, nostalgia, xenophobia, and anti-abortion voters . The populist angle is looking fairly broken now between anti-populist tax cuts and widely publicized efforts to all...

We are hiring for reinforcement learning related research at all levels and all MSR labs. If you are interested, apply, talk to me at COLT or ICML , or email me. More generally though, I wanted to lay out a philosophy of research which differs from (and plausibly improves on) the current prevailing mode. Deepmind and OpenAI have popularized an empirical approach where researchers modify algorithms and test them against simulated environments, including in self-play. They’ve achieved significant success in these simulated environments, greatly expanding the reportoire of ‘games solved by reinforcement learning’ which consisted of the singleton backgammon when I was a graduate student. Given the ambitious goals of these organizations, the more general plan seems to be “first solve games, then solve real problems”. There are some weaknesses to this approach, which I want to lay out next. Broken API One issue with this is that multi-step reinforcement learning is a broken API in...

Consider the following facts: NIPS submission are up 50% this year to ~4800 papers. There is significant evidence that the process of reviewing papers in machine learning is creaking under several years of exponentiating growth. Public figures often overclaim the state of AI. Money rains from the sky on ambitious startups with a good story. Apparently, we now even have a fake conference website ( https://nips.cc/ is the real one for NIPS). We are clearly not in a steady-state situation. Is this a bubble or a revolution? The answer surely includes a bit of revolution—the fields of vision and speech recognition have been turned over by great empirical successes created by deep neural architectures and more generally machine learning has found plentiful real-world uses. At the same time, I find it hard to believe that we aren’t living in a bubble. There was an AI bubble in the 1980s (before my time), a techbubble around 2000, and we seem to have a combined AI/tech bubble going ...

If you are interested in building an industrial Reinforcement Learning platform, we are hiring a data scientist and multiple developers as a followup to last year’s hiring . Please apply if interested as this is a real chance to be a part of building the future Machine Learning (Theory) https://ift.tt/2HtZVlU April 16, 2018 at 11:17PM

The outcome of the election for the IMLS (which runs ICML ) adds Emma Brunskill , Kamalika Chaudhuri , and Hugo Larochelle to the board. The current members of the board (and the reason for board membership) are: Andreas Krause (Elected & 2018 Program Chair) Andrew McCallum (Past president) Bernhard Schoelkopf (Elected) Corinna Cortes (Elected) David Blei (2020 General chair) Doina Precup (2017 Program Chair) Emma Brunskill (Elected) Eric Xing (Elected & 2019 General Chair) Francis Bach (Elected & 2018 General Chair) Hanna Wallach (Elected) Hugo Larochelle (Elected) Jennifer Dy (2018 Program Chair & Secretary) Joelle Pineau (Elected & President) Kamalika Chaudhuri (Elected & 2019 Program Chair) John Langford (President Elect & 2016 General Chair) Kilian Weinberger (Elected & 2016 Program Chair) Nina Balcan (Elected & 2021 General Chair & 2016 Program Chair) Ruslan Salakhutdinov (Elected & 2019 Program Ch...

The surge of interest in reinforcement learning is great fun, but I often see confused choices in applying RL algorithms to solve problems. There are two purposes for which you might use a world simulator in reinforcement learning: Reinforcement Learning Research : You might be interested in creating reinforcement learning algorithms for the real world and use the simulator as a cheap alternative to actual real-world application. Problem Solving : You want to find a good policy solving a problem for which you have a good simulator. In the first instance I have no problem, but in the second instance, I’m seeing many head-scratcher choices. A reinforcement learning algorithm engaging in policy improvement from a continuous stream of experience needs to solve an opportunity-cost problem. (The RL lingo for opportunity-cost is “advantage”.) Thinking about this in the context of a 2-person game, at a given state, with your existing rollout policy, is taking the first action leading to...

Yesterday, I tagged VW version 8.5.0 which has many interactive learning improvements (both contextual bandit and active learning), better support for sparse models, and a new baseline reduction which I’m considering making a part of the default update rule. If you want to know the details, we’ll be doing a mini-tutorial during the Friday lunch break at the Extreme Classification workshop at NIPS . Please join us if interested. Machine Learning (Theory) http://ift.tt/2BHVPRJ December 03, 2017 at 08:45PM

Alekh and I have been polishin the Real World Interactive Learning tutorial for ICML 2017 on Sunday. This tutorial should be of pretty wide interest. For data scientists, we are crossing a threshold into easy use of interactive learning while for researchers interactive learning is plausibly the most important frontier of understanding. Great progress on both the theory and especially on practical systems has been made since an earlier NIPS 2013 tutorial . Please join us if you are interested Machine Learning (Theory) http://ift.tt/2waGzdh August 03, 2017 at 11:18PM

Andrew McCallum has been leading an initiative to update the bylaws of IMLS , the organization which runs ICML . I expect most people aren’t interested in such details. However, the bylaws change rarely and can have an impact over a long period of time so they do have some real importance. I’d like to hear comment from anyone with a particular interest before this year’s ICML. In my opinion, the most important aspect of the bylaws is the at-large election of members of the board which is preserved. Most of the changes between the old and new versions are aimed at better defining roles, committees, etc… to leave IMLS/ICML better organized. Anyways, please comment if you have a concern or thoughts. Machine Learning (Theory) http://ift.tt/2trivVB July 19, 2017 at 11:01PM