NIPS Reviewer Recruitment and ‘Experience’

Triggered by a question from Christoph Lampert as a comment on a previous blog post on reviewer allocation, I thought I’d post about how we did reviewer recruitment, and what the profile of reviewer ‘experience’ is, as defined by their NIPS track record.

I wrote this blog post, but it ended up being quite detailed, so Corinna suggested I put the summary of reviewer recruitment first, which makes a lot of sense. If you are interested in the details of our reviewer recruitment, please read on to the section below ‘Experience of the Reviewing Body’.

Questions

As a summary, I’ve imagined two questions and given answers below:

  1. I’m an Area Chair for NIPS, how did I come to be invited?
    You were personally known to one of the Program Chairs as an expert in your domain who had good judgement about the type and quality of papers we are looking to publish at NIPS. You have a strong publication track record in your domain. You were known to be reliable and responsive. You may have a track record of workshop organization in your domain and/or experience in area chairing previously at NIPS or other conferences. Through these activities you have shown community leadership.
  2. I’m a reviewer for NIPS, how did I come to be invited?
    You could have been invited for one of several reasons:

    • you were a reviewer for NIPS in 2013
    • you were a reviewer for AISTATS in 2012
    • you were personally recommended by an Area Chair or a Program Chair
    • you have been on a Program Committee (i.e. you were an Area Chair) at a leading international conference in recent years (specifically NIPS since 2000, ICML since 2008, AISTATS since 2011).
    • you have published 2 or more papers at NIPS since 2007
    • you published at NIPS in either 2012 or 2013 and your publication track record was personally reviewed and approved by one of the Program Chairs.

Experience of The Reviewing Body

That was the background to Reviewer and Area Chair recruitment, and it is also covered below, in much more detail than perhaps anyone could wish for! Now, for those of you that have gotten this far, we can try and look at the result in terms of one way of measuring reviewer experience. Our aim was to increase the number of reviewers and try and maintain or increase the quality of the reviewing body. Of course quality is subjective, but we can look at things such as reviewer experience in terms of how many NIPS publications they have had. Note that we have purposefully selected many reviewers and area chairs who have never previously published at NIPS, so this is clearly not the only criterion for experience, but it is one that is easily available to us and given Christoph’s question, the statistics may be of wider interest.

Reviewer NIPS Publication Record

Firstly we give the histograms for cumulative reviewer publications. We plot two histograms, publications since 2007 (to give an idea of long term trends) and publications since 2012 (to give an idea of recent trends).

Reviewer Publications 2007

Histogram of NIPS 2014 reviewers publication records since 2007.

Our most prolific reviewer has published 22 times at NIPS since 2007! That’s an average of over 3 per year (for comparison, I’ve published 7 times at NIPS since 2007).

Looking more recently we can get an idea of the number of NIPS publications reviewers have had since 2012.

Histogram of NIPS 2014 reviewers publication records since 2012.

Impressively the most prolific reviewer has published 10 papers at NIPS over the last two years, and intriguingly it is not the same reviewer that has published 22 times since 2007. The mode of 0 reviews is unsurprising, and comparing the histograms it looks like about 200 of our reviewing body haven’t published in the last two years, but have published at NIPS since 2007.

Area Chair Publication Record

We have got similar plots for the Area Chairs. Here is the histogram since 2007.

Area Chair Publications 2007

Histogram of NIPS 2014 Area Chair’s publication records since 2007.

Note that we’ve selected 16 Area Chairs who haven’t published at NIPS before. People who aren’t regular to NIPS may be surprised at this, but I think it reflects the openness of the community to other ideas and new directions for research. NIPS has always been a crossroads between traditional fields, and that is one of it’s great charms. As a result, NIPS publication record is a poor proxy for ‘experience’ where many of our area chairs are concerned.

Looking at the more recent publication track record for Area Chairs we have the following histogram.

Area Chair Publications 2012

Histogram of NIPS 2014 Area Chair’s publication records since 20012.

Here we see that a considerable portion of our Area Chairs haven’t published at NIPS in the last two years. I also find this unsurprising. I’ve only published one paper at NIPS since then (that was NIPS 2012, the groups’ NIPS 2013 submissions were both rejected—although I think my overall ‘hit rate’ for NIPS success is still around 50%).

Details of the Recruitment Process

Below are all the gritty details in terms of how things actually panned out in practice for reviewer recruitment. This might be useful for other people chairing conferences in the future.

Area Chair Recruitment

The first stage is invitation of area chairs. To ensure we got the correct distribution of expertise in area chairs, we invited in waves. Max and Zoubin gave us information about the subject distribution of the previous year’s NIPS submissions. This then gave us a rough number of area chairs required for each area. We had compiled a list of 99 candidate area chairs by mid January 2014, coverage here matched the subject coverage from the previous year’s conference. The Area Chairs are experts in their field, the majority of the Area Chairs are people that either Corinna or I have worked with directly or indirectly, others have a long track record of organising workshops and demonstrating thought leadership in their subject area. It’s their judgement on which we’ll be relying for paper decisions. As capable and active researchers they are in high demand for a range of activities (journal editing, program chairing other conferences, organizing workshops etc). This combined with the demands on our everyday lives (including family illnesses, newly born children etc) mean that not everyone can accept the demands on time that being an area chair makes. As well as being involved in reviewer recruitment, assignment and paper discussion. Area chairs need to be available for video conference meetings to discuss their allocation and make final recommendations on their papers. All this across periods of the summer when many are on vacation. Of our original list of 99 invites, 56 were available to help out. This then allowed us to refocus on areas where we’d missed out on Area Chairs. By early March we had a list of 57 further candidate area chairs. Of these 36 were available to help out. Finally we recruited a further 3 Area Chairs in early April, targeted at areas where we felt we were still short of expertise.

Reviewer Recruitment

Reviewer recruitment consists of identifying suitable people and inviting them to join the reviewing body. This process is completed in collaboration with the Area Chairs, who nominate reviewers in their domains. For NIPS 2014 we were targeting 1400 reviewers to account for our duplication of papers and the anticipated increase in submissions. There is no unified database of machine learning expertise, and the history of who reviewed in what years for NIPS is currently not recorded. This means that year to year, we are typically only provided with those people that agreed to review in the previous year as our starting point for compiling this list. From February onwards Corinna and I focussed on increasing this starting number. NIPS 2013 had 1120 reviewers and 80 area chairs, these names formed the core starting point for invitations. Further,  since I program chaired AISTATS in 2012 we also had the list of reviewers who’d agreed to review for that conference (400 reviewers, 28 area chairs). These names were also added to our initial list of candidate reviewers (although, of course, some of these names had already agreed to be area chairs for NIPS 2014 and there were many duplicates in the lists).

Sustaining Expertise in the Reviewing Body

A major concern for Corinna and I was to ensure that we had as much expertise in our reviewing body as possible. Because of the way that reviewer names are propagated from year to year, and the fact that more senior people tend to be busier and therefore more likely to decline, many well known researcher names weren’t in this initial list. To rectify this we took from the web the lists of Area Chairs for all previous NIPS conferences going back to 2000, all ICML conferences going back to 2008 and all AISTATS conferences going back to 2011. We could have extended this search to COLT, COSYNE and UAI also. Back in 2000 there were only 13 Area Chairs at NIPS, by the time that I first did the job in 2005 there were 19 Area Chairs. Corinna and I worked together at the last Program Committee to have a physical meeting in 2006 when John Platt was Program Chair. I remember having an above-average allocation of about 50-60 papers as Area Chair that year. I had papers on Gaussian processes (about 20) and many more in dimensionality reduction, mainly on spectral approaches. Corinna also had a lot of papers that year because she was dealing with kernel methods. Although I think a more typical load was 30-40, and reviewer load was probably around 6-8. The physical meeting consisted of two days in a conference room discussing every paper in turn as a full program committee.  That was also the last year of a single program chair. The early NIPS program committees mainly read as a “who’s who of machine learning”, and it sticks in my mind how carefully each chair went through each of the papers that were around the borderline of acceptance. Many papers were re-read at that meeting. Overall 160 new names were added to the list of candidate reviewers from incorporating the Area Chairs from these meetings, giving us around 1600 candidate reviewers in total. Note that the sort of reviewing expertise we are after is not only the technical expertise necessary to judge the correctness of the paper. We are looking for reviewers that can judge whether the work is going to be of interest to the wider NIPS community and whether the ideas in the work are likely to have significant impact. The latter two areas are perhaps more subjective, and may require more experience than the first. However, the quality of papers submitted to NIPS is very high, and the number that are technically correct is a very large portion of those submitted. The objective of NIPS is not then to select those papers that are the ‘most technical’, but to select those papers that are likely to have an influence on the field. This is where understanding of likely impact is so important. To this end, Max and Zoubin introduced an ‘impact’ score, with the precise intent of reminding reviewers to think about this aspect. However, if the focus is too much on the technical side, then maybe a paper that is highly complex from a technical stand-point, but less unlikely to have an influence on the direction of the field, is more likely to be accepted than a paper that contains a potentially very influential idea that doesn’t present a strong technical challenge. Ideally then, a paper should have a distribution of reviewers who aren’t purely experts in the particular technical domain from where the paper arises, but also informed experts in the wider context of where the paper sits. The role of the Area Chair is also important here. The next step in reviewer recruitment was to involve the Area Chairs in adding to the list in areas where we had missed people. This is also an important route for new and upcoming NIPS researchers to become involved in reviewing. We provided Area Chairs with access to the list of candidate reviewers and asked them to add names of experts who they would like to recruit, but weren’t currently in the list. This led to a further 220 names.

At this point we had also begun to invite reviewers. Reviewer invitation was done in waves. We started with the first wave of around 1600-1700 invites in mid-April. At that point, the broad form of the Program Committee was already resolved. Acceptance rates for reviewer invites indicated that we weren’t going to hit our target of 1400 reviewers with our candidate list. By the end of April we had around 1000 reviewers accepted, but we were targeting another 400 reviewers to ensure we could keep reviewer load low.

A final source of candidates was from Chris Hiestand. Chris maintains the NIPS data base of authors and presenters on behalf of the NIPS foundation. This gave us another potential source of reviewers. We considered all authors that had 2 or more NIPS papers since 2007. We’d initially intended to restrict this number to 3, but that gained us only 91 more new candidate reviewers (because most of the names were in our candidate list already), relaxing this constraint to 2 led to 325 new candidate reviewers. These additional reviewers were invited at the end of April. However, even with this group, were likely to fall short of our target.

Our final group of reviewers came from authors who published either at NIPS 2013 or NIPS 2012. However, authors that have published only one paper are not necessarily qualified to review at NIPS. For example, the author may be a collaborator from another field. There were 697 authors who had one NIPS paper in 2012 or 2013 and were not in our current candidate list. For these 697 authors, we felt it was necessary to go through each author individually, checking their track record on through web searches (DBLP and Google Scholar as well as web pages) and ensuring they had the necessary track record to review for NIPS. This process resulted in an additional 174 candidate reviewer names. The remainder we either were unable to identify on the web (169 people) or they had a track record where we couldn’t be confident about their ability to review for NIPS without a personal recommendation (369 people).  This final wave of invites went out at the beginning of May and also included new reviewer suggestions from Area Chairs and invites to candidate Area Chairs who had not been able to commit to Area Chairing, but may have been able to commit to reviewing. Again, we wanted to ensure the expertise of the reviewing body was as highly developed as possible.

This meant that by the submission deadline we had 1390 reviewers in the system. On 15th July this number has increased slightly. This is because during paper allocation, Area Chairs have recruited additional specific reviewers to handle particular papers where they felt that the available reviewers didn’t have the correct expertise. This means that currently, we have 1400 reviewers exactly. This total number of reviewers comes from around 2255 invitations to review.

Overall, reviewer recruitment took up a very large amount of time, distributed over many weeks. Keeping track of who had been invited already was difficult, because we didn’t have a unique ID for our candidate reviewers. We have a local SQLite data base that indexes on email, and we try to check for matches based on names as well. Most of these checks are done in Python code which is now available on the github repository here, along with IPython notebooks that did the processing (with identifying information removed). Despite care taken to ensure we didn’t add potential reviewers twice to our data base, several people received two invites to review. Very often, they also didn’t notice that they were separate invites, so they agreed to review twice for NIPS. Most of these duplications were picked up at some point before paper allocation and they tended to arise for people whose names could be rendered in multple ways (e.g. because of accents)  who have multiple email addresses (e.g. due to change of affiliation).

 

Firstly, NIPS uses the CMT system for conference management. In an ideal world, choice of management system shouldn’t dictate how you do things, but in practice particularities of the system can affect our choices. CMT doesn’t store a uniques profile for conference reviewers (unlike for example EasyChair which stores every conference you’ve submitted to or reviewed/chaired for). This means that from year to year information about the previous years reviewers isn’t necessarily passed in a consistent way between program chairs. Corinna and I requested that the CMT set up for our year copied across the reviewers from NIPS 2013 along with their subject areas and conflicts to try and alleviate this. The NIPS program committee in 2013 consisted of 1120 reviewers and 80 area chairs. Corinna and I set a target of 1400 reviewers and 100 area chairs. This was to account for (a) increase in submissions of perhaps 10% and (b) duplication of papers for independent reviewing at a level of around 10%.

Open Data Science

Not sure if this is really a blog post, it’s more of a ‘position paper’ or a proposal, but it’s something that I’d be very happy to have comment on, so publishing it in the form of a blog seems most appropriate.

We are in the midst of the information revolution and it is being driven by our increasing ability to monitor, store, interconnect and analyse large interacting sets of data. Industrial mechanisation required a combination of coal and heat engine. Informational mechanisation requires the combination of data and data engines. By analogy with a heat engine, which takes high entropy heat energy, and converts it to low entropy, actionable, kinetic energy, a data engine is powered by large unstructured data sources and converts them to actionable knowledge. This can be achieved through a combination of mathematical and computational modelling and the combination of required skill sets falls across traditional academic boundaries.

Outlook for Compaines

From a commercial perspective companies are looking to characterise consumers/users in unprecedented detail. They need to characterize their users’ behavior in detail to

  1. provide better service to retain users,
  2. target those users with commercial opportunities.

These firms are competing for global dominance, to be the data repository. They are excited by the power of interconnected data, but made nervous about the natural monopoly that it implies. They view the current era as being analogous to the early days of ‘microcomputers’: competing platforms looking to dominate the market. They are nervous of the next stage in this process. They foresee the natural monopoly that the interconnectedness of data implies, and they are pursuing it with the vigour of a young Microsoft. They are paying very large fees to acquire potential competitors to ensure that they retain access to the data (e.g. Facebook’s purchase of Whatsapp for $19 billion) and they are acquiring expertise in the analysis of data from academia either through direct hires (Yann LeCun from NYU to Facebook, Andrew Ng from Stanford to found a $300 million Research Lab for Baidu) or purchasing academic start ups (Geoff Hinton’s DNNResearch from Toronto to Google, the purchase of DeepMind by Google for $400 million). The interest of these leading internet firms in machine learning is exciting and a sign of the major successes of the field, but it leaves a major challenge for firms that want to enter the market and either provide competing or introduce new services. They are debilitated by

  1. lack of access to data,
  2. lack of access to expertise.

 

Science

Science is far more evolved than the commercial world from the perspective of data sharing. Whilst its merits may not be
universally accepted by individual scientists, communities and funding agencies encourage widespread sharing. One of the most significant endeavours was the human genome project, now nearly 25 years old. In computational biology there is now widespread sharing of data and methodologies: measurement technology moves so quickly that an efficient pipeline for development and sharing is vital to ensure that analysis adapts to the rapidly evolving nature of the data (e.g. cDNA arrays to Affymetrix arrays to RNAseq). There are also large scale modelling and sharing challenges at the core of other disciplines such as astronomy (e.g. Sarah Bridle’s GREAT08 challenge for Cosmic Lensing) and climate science. However, for many scientists their access to these methodologies is restricted not by lack of availability of better methods, but through technical inaccessibility. A major challenge in science is bridging the gap between the data analyst and the scientist. Equipping the scientist with the fundamental concepts that will allow them to explore their own systems with a complete mathematical and computational toolbox, rather than being constrained by the provisions of a commercial ‘analysis toolbox’ software provider.

Health

Historically, in health, scientists have worked closely with clinicians to establish the cause of disease and, ideally, eradicate
them at source. Antibiotics and vaccinations have had major successes in this area. The diseases that remain are

  1. resulting from a large range of initial causes; and as a result having no discernible target for a ‘magic bullet’ cure (e.g. heart disease, cancers).
  2. difficult to diagnose at early stage, leading to identification only when progress is irreversible (e.g. dementias) or
  3. coevolving with our clinical advances developments to subvert our solutions (e.g. C difficile, multiple drug resistant tuberculosis).

Access to large scale interconnected data sources again gives the promise of a route to resolution. It will give us the ability to better characterize the cause of a given disease; the tools to monitor patients and form an early diagnosis of disease; and the biological
understanding of how disease agents manage to subvert our existing cures. Modern data allows us to obtain a very high resolution,
multifaceted perspective on the patient. We now have the ability to characterise their genotype (through high resolution sequencing) and their phenotype (through gene and protein expression, clinical measurements, shopping behaviour, social networks, music listening behaviour). A major challenge in health is ensuring that the privacy of patients is respected whilst leveraging this data for wider societal benefit in understanding human disease. This requires development of new methodologies that are capable of assimilating these information resources on population wide scales. Due to the complexity of the underlying system, the methodologies required are also more complex than the relatively simple approaches that are currently being used to, for example, understand commercial intent. We need more sophisticated and more efficient data engines.

International Development

The wide availability of mobile telephones in many developing countries provides opportunity for modes of development that differ considerably from the traditional paths that arose in the past (e.g. canals, railways, roads and fixed line telecommunications). If countries take advantage of these new approaches, it is likely that the nature of the resulting societies will be very different from those that arose through the industrial revolution. The rapid adoption of mobile money, which arguably places parts of the financial system in many sub-saharan African countries ahead of their apparently ‘more developed’ counterparts, illustrates what is possible. These developments are facilitated by low capital cost of deployment. They are reliant on the mobile telecommunications architecture and the widespread availability of handsets. The ease of deployment and development of mobile phone apps, and the rapidly increasing availability of affordable smartphone handsets presents opportunities that exploit the particular advantages of the new telecommunications ecosystem. A key strand to our thinking is that these developments can be pursued by local entrepeneurs and software developers (to see this in action check out the work of the AI-DEV group here). The two main challenges for enabling this to happen are mechanisms for data sharing that retain the individual’s control over their data and the education of local researchers and students. These aims are both facilitated by the open data science agenda.

Common Strands to these Challenges

The challenges described above have related strands to them that can be summarized in three areas:

  1. Access to data whilst balancing the individual’s right to privacy alongside the societal need for advance.
  2. Advancing methodologies: development of methodologies needed to characterize large interconnected complex data sets
  3. Analysis empowerment: giving scientists, clinicians, students, commercial and academic partners the ability to analyze their own data using the latest methodological advances.

The Open Data Science Idea

It now seems absurd to posit a ‘magic bullet cure’ for the challenges described above across such diverse fields, and indeed, the underlying circumstances of each challenge is sufficiently nuanced for any such sledge hammer to be brittle. However, we will attempt to describe a philosophical approach, that when combined with the appropriate domain expertise (whether that’s cultural, societal or technical)  will aim to address these issues in the long term.

Microsoft’s quasi-monopoly on desk top computing was broken by open source software. It has been estimated that the development cost of a full Linux system would be $10.8 billion dollars. Regardless of the veracity of this figure, we know that
several leading modern operating systems are based on open source (Android is based on Linux, OSX is based on FreeBSD). If it weren’t for open source software, then these markets would have been closed to Microsoft’s competitors due to entry costs. We can do much to celebrate the competition provided by OSX and Android and the contributions of Apple and Google in bringing them to market, but the enablers were the open source software community. Similarly, at launch both Google and Facebook’s architectures, for web search and social networking respectively, were entirely based on open source software and both companies have contributed informally and formally to its development.

Open data science aims to bring the same community resource assimilation together to capitalize on underlying social driver of this phenomenon: many talented people would like to see their ideas and work being applied for the widest benefit possible. The modern internet provides tools such as github, IPython notebook and reddit for easily distribution and comment on this material. In Sheffield we have started making our ideas available through these mechanisms. As academics in open data science part of our role should be to:

  1. Make new analysis methodologies available as widely and rapidly as possible with as few conditions on their use as possible
  2. Educate our commercial, scientific and medical partners in the use of these latest methodologies
  3. Act to achieve a balance between data sharing for societal benefit and the right of an individual to own their data.

We can achieve 1) through widespread distribution of our ideas under flexible BSD-like licenses that give commercial, scientific and medical partners as much flexibility to adapt our methods and analyses as possible to their own circumstances. We will achieve 2) through undergraduate courses, postgraduate courses, summer schools and widespread distribution of teaching materials. We will host projects from across the University from all departments. We will develop new programs of study that address the gaps in current expertise. Our actions regarding 3) will be to support and advise initiatives which look to return to the individual more control of their own data. We should do this simultaneously with engaging with the public on what the technologies behind data sharing are and how they will benefit society.

Summary

Open data science should be an inclusive movement that operates across traditional boundaries between companies and academia. It could bridge the technological gap between ‘data science’ and science. It could address the barriers to large scale analysis of health data and it will build bridges between academia and companies to ease access to methodologies and data. It will make our ideas publicly available for consumption by the individual; in developing countries, commercial organisations and public institutes.

In Sheffield we have already been actively pursuing this agenda through different strands: we have been making software available for over a decade, and now are doing so with extremely liberal licenses. We are running a series of Gaussian process summer schools, which have included roadshows in UTP, Colombia (hosted by Mauricio Alvarez) and Makerere University, Uganda (hosted by John Quinn). We have organised workshops targeted at Big Data and we are making our analysis approaches freely available. We have organised courses locally in Sheffield in programming targeted at biologists (taught by Marta Milo) and have begun a series of meetings on Data Science (speakers have included Fernando Perez, Fabian Pedregosa, Michael Betancourt and Mike Croucher). We have taught on the ML Summer School and at EBI Summer Schools focused on Computational Systems Biology. Almost all these activities have led to ongoing research collaborations, both for us and for other attendees. Open Data Science brings all these strands together, and it expands our remit to communicate using the latest tools to a wider cross section of clinicians and scientists. Driven by this agenda we will also expand our interaction with commercial partners, as collaborators, consultants and educators. We welcome other groups both in the UK and internationally in joining us in achieving these aims.