How to organize a Workshop?

It is key that you organize a workshop per year as Post-Doc and one in your Ph.D. as a Ph.D. student. Here is the most relevant advice on how to do that!

Key Aspects

Make sure when you plan a workshop that:

Its should be a HERZENSTHEMA (German for a subject close to your heart) which highly linked to your research
You choose that really, really interests you and not one which is "cool."
Ideally, you want to publish on this topic in the year and/or submit a proposal on it.
Make sure that it is sufficiently FOCUSSED (It's not a conference) and narrow. The title needs to sound completely novel.
Use TASTE for the topic and the implied research questions. Do the workshop for helping you making progress on the topic, take the getting more well-known as a beneficial side aspect.
The more you get out of the workshop for your research questions, the better the workshop will appear to others and the more credit and PR will you get out of it.
Don't cater to other people! What you want to learn counts! You are organizing a tutorial for yourself.
When you choose your co-organizers, make sure that they are not just your old friends but sufficiently different from you. Ideally, they are older and more famous than you, helping you shine. Never ask more than three other ppl. If you can distribute geographically (European, American and Australian-Pacific-Asian).
It can be useful to have a Steering Committee of older and more famous ppl -- both to make the proposal more attractive as well as to share the blame if you need to do a painful decision.
A call for posters/abstracts can help getting sufficiently many participants!

Applying for a Workshop

When applying for a workshop, it is key to transmit to the workshop chairs that we know exactly what we are doing. I have attached a proposal from past workshops. Only few workshops that followed that format has ever been rejected. Key elements are:

a good list of tentative speakers (select them because they fit the topic not because they are famous and only select political valuable speakers as exceptions)
a list of relevant open questions which really interest you to solve in the future
a good idea on the target audience
a call for posters to ensure sufficient participation / large audiences
a workshop website (see NIPS06 Towards a New RL! for an example) with a "quick facts list"
an exciting abstract (like the ones in your PDF)
make sure your co-organizers are helpful, nice and a little bit older than you are (but not by much).
do not organize with the same people workshops repeatedly on the same topic

Note that a few workshop chairs do not appreciate PDFs oder Word workshop proposals. Ask them real quick what they prefer - or if you don't ask, better send plain text.

Inviting your tentative invited speaker

If your list of tentative speakers looks excellent for your HERZENSTHEMA research topic, you need to ask them whether you could add their names to the proposal. Please make sure to send them an email like

  Dear YYY, 
  we would like to invite you to give a talk in our planned workshop
  

  "XXXXXXXXXXX",
  

  which will be suggested to the conference organizers of NIPS
  Neural Information Processing Systems 20ZZ (at Westin Resort
  and Spa and Westin Hilton, Whistler, B.C., Canada). The workshop
  will be organized by UUU, VVV  and WWW. Please see the
  attached abstract for more information.
  

  At this point, we just wanted to ask you whether we could list you as 
  a tentative speaker -- no real commitment is necessary at this time. 
  We would also be glad about any suggestions for additional 
  prospective participants for the workshop. 
  Please note that we will not be able to provide travel funds and 
  reimbursement for potential conference fees for this workshop. 
  Best wishes, 
  

  UUU, VVV  and WWW

That way everybody will like to be listed -- even if they cannot make it. Rather ask too many people than too few -- you are getting your name out by asking people and it will pay off a few years down the road.

The communication can be tedious and copy + paste loops can be dangerous. I strongly recommend to get a program like SerialMailer to ensure that you do not embarrass yourself.

Make sure to CC your co-organizers on all communication.

Example Proposal

This kind of example proposal has worked well in the past. Submit it text only, no PDFs or Word Files.

  ***************************************************************
  

  WORKSHOP PROPOSAL
  Title: Robotics Challenges for Machine Learning
  

  Organizers:
  Marc Toussaint (Technical University of Berlin),
  Jan Peters (Max Planck Institute for Biological Cybernetics)
  

  WWW: http://www.robot-learning.de 
  

  Abstract:
  Creating autonomous robots that can assist humans in situations of daily life is a great challenge
  for machine learning. While this aim has   been a long standing vision of robotics, artificial intelligence,
 and the cognitive sciences, we have yet to achieve the first step of creating robots that can accomplish
 a multitude of different tasks, triggered by environmental context or higher level instruction. Despite
 the wide range of machine learning problems encountered in robotics, the main bottleneck towards
 this goal has been a lack of interaction between the core robotics and the machine learning communities.
 To date, many roboticists still discard machine learning approaches as generally inapplicable or inferior
 to classical, hand-crafted solutions. Similarly, machine learning researchers do not yet acknowledge
 that robotics can play the same role for machine learning which for instance physics had for mathematics:
 as a major application as well as a driving force for new ideas, algorithms and approaches.
  

  Some fundamental problems we encounter in robotics that equally inspire current research directions in Machine Learning are:
  -- learning and handling models, (e.g., of robots, task or environments)
  -- learning deep hierarchies or levels of representations (e.g., from sensor & motor representations to task abstractions)
  -- regression in very high-dimensional spaces for model and policy learning 
  -- finding low-dimensional embeddings of movement as an implicit generative model
  -- methods for probabilistic inference of task parameters from vision,e.g., 3D geometry of manipulated objects 
  -- the integration of multi-modal information (e.g., proprioceptive, tactile, vison) for state estimation and causal inference
  -- probabilistic inference in non-linear, non-Gaussian stochastic systems (e.g., for planning as well as optimal or adaptive control)
  Robotics challenges can inspire and motivate new Machine Learning research as well as being an interesting field of application of standard ML techniques.
  

  Inversely, with the current rise of real, physical humanoid robots in robotics research labs around the globe,
 the need for machine learning in robotics has grown significantly. Only if machine learning can succeed at
 making robots fully adaptive, it is likely that we will be able to take real robots out of the research labs into
 real, human inhabited environments.
  To do so, we future robots will need to be able to make proper use of perceptual stimuli such as vision,
 proprioceptive & tactile feedback and translate these into motor commands.
  

  To close this complex loop, machine learning will be needed on various stages ranging from sensory-based
 action determination over high-level plan generation to motor control on torque level. Among the important
 problems hidden in these steps are problems which can be understood from the robotics and the machine
 learning point of view including perceptuo-action coupling, imitation learning, movement decomposition,
 probabilistic planning problems, motor primitive learning, reinforcement learning, model learning and
 motor control.
  

  Format:
  The goal of this workshop is to bring together people that are interested in robotics as a source and inspiration
 for new Machine Learning challenges, or which work on Machine Learning methods as a new approach to
 robotics challenges. In the robotics context, among the questions which we intend to tackle are
  

  Reinforcement Learning, Imitation, and Active Learning:
  * What methods from reinforcement learning scale into the domain of robotics?
  * How can we improve our policies acquired through imitation by trial and error?
  * Can we turn many simple learned demonstrations into proper policies?
  * Does the knowledge of the cost function of the teacher help the student?
  * Can statistical methods help for generating actions which actively influencing our
 perception? E.g., Can these be used to plan visuo-motor sequences that will minimize
 our uncertainty about the scene?
  * How can image understanding methods be extended to provide probabilistic scene
 descriptions suitable for motor planning?
  

  Motor Representations and Control: 
  * Can we decompose human demonstations into elemental movements, e.g.,
 motor primitives, and learn these efficiently?
  * Is it possible to build libraries of basic movements from demonstration? 
 How to create higher-level structured representations and abstractions based on elemental movements?
  * Can structured (e.g., hierarchical) temporal stochastic models be used to plan the
 sequencing and superposition of movement primitives?
  * Is probabilistic inference the road towards composing complex action sequences from
 simple demonstrations? Are superpositions of motor primitives and the coupling in timing
 between these learnable?
  * How to generate compliant controls for executing complex movement plans which
 include both superposition and hierachies of elemental movements? Can we find learned
 versions of priortized hierachical control?
  * Can we learn how to control in task-space of redundant robots in the presence of
 underactuation and complex constraints? Can we learn force or hybrid control in task-space?
  * Is real-time model learning the way to cope with executing tasks on robots with
 unmodeled nonlinearities and manipulating uncertain objects in unpredictable environmental interactions?
  * What new regression techniques can help real-time model learning to improve the
 execution of tasks on robots with unmodeled nonlinearities and manipulating uncertain
 objects in unpredictable environmental interactions?
  

  Learning structured models and representations:
  * What kind of probabilistic models provide a compact and suitable description of
 real-world environments composed of manipulable objects?
  * How can abstractions or compact representations be learnt from sensori-motor data?
  * How can we extract features of the sensori-motor data that are relevant for motor
 control or decision making? E.g., can we extract visual features of objects directly related
 to their manipulability or ``affordance''?
  

  Confirmed Tentative Speakers:
  Auke Ijspeert, Aude Billard, Sethu Vijayakumar, Andrew Ng, Joelle Pineau, Christopher
 G. Atkeson, Drew Bagnell, Nikos Vlassis, Stefan Schaal, Emo Todorov, Sridhar Mahadevan,
 Russ Tedrake, Nando de Freitas
  

  Program:
  TBA
  

  Posters:
  There will be a call for posters upon acceptance of the workshop. The deadline for
 abstract submissions will be October 21, 2007 and the notification will be October 26, 2007.
  

  Participants:
  This workshop will bring together researchers from both the robotics and machine learning
 in order to explore how to approach the topic of solving the current statistical learning challenges
 in robotics in a principled way. Participants of the workshop (inclusive of the audience) are
 encouraged to actively participate by responding with questions and comments about the talks
 and give stand-up talks. Please contact the organizers if you would like to reserve a priori some
 time for expressing your view on a particular topic.
  

  Organizers:
  The workshop is organized by Marc Toussaint (http://ml.cs.tu-berlin.de/~mtoussai),
 Technical University of Berlin, Germany, and by Jan Peters (http://www.jan-peters.net),
 Max-Plank Institute for Biological Cybernetics, Tuebingen, Germany & University of Southern California (USC).
  

  CV of Organizer Marc Toussaint:
  Marc Toussaint joined the Machine Learning group at Technical University Berlin (Prof. Klaus-Robert Mueller)
 in Mai 2007 as PI of the Machine Learning & Robotics Lab funded by the Emmy Noether excellence grant.
 Before that, he spend 2 years as a post-doc at the School of Informatics in Edinburgh (Prof. Chris Williams
 & Prof. Sethu Vijayakumar) and 6 months at the Robotics lab of Honda Research Institute (Offenbach, Germany).
 In 2003, Marc graduated with a PhD from University of Bochum, Germany, and he received a Diploma
 in Theoretical Physics in 1999 and a Pre-Diploma in Mathematics in 1996 at the University of Cologne. Marc
 collected extensive experience in Machine Learning methods and their application to real-world Robotics
 during his stay in Edinburgh and the Honda Research Institute. His research centers around learning
 appropriate representations for stochastic search, reasoning and motor control, and the use of
 probabilistic (inference) methods for solving (PO)MDPs and motor control/planning problems.
  

  CV of Organizer Jan Peters:
  Jan Peters joined the Max-Planck Institute for Biological Cybernetics in April 2007 as a research
 scientist and is currently heading the Robot Learning Lab in the Departement of Bernhard Schoelkopf.
 Before joining MPI, Jan Peters graduated with a Ph.D. in Computer Science from University of Southern
 California (USC) with the thesis committee of Stefan Schaal, Firdaus Udwadia, Gaurav Sukhatme, and
 Chris Atkeson (CMU) and remains affiliated with the Computational Learning and Motor Control Lab
 at USC as an adjunct researcher. Before his Ph.D., he received a German M.Sc. in Informatics from
 University of Hagen in 2000, a German M.Sc. in Electrical Engineering from  Munich University of
 Technology (TU Muenchen) in 2001, a M.Sc. in Computer Science in 2002 and M.Sc. in Mechanical 
 Engineering in 2005, both from USC. Jan Peters has extensive experience in machine learning
 for robotics. Additionally to his work at the MPI and USC, he has spent several years performing
 robot learning research at the Department of Robotics at the German Aerospace Research
 Center (DLR) in Germany, at the Robotics & Control Division at Siemens Advanced Engineering
 in Singapore, at the Department of Humanoid Robotics and Computational Neuroscience at the
 Advanced Telecommunication Research (ATR) Center in Japan, as well as the National University
 of Singapore (NUS). Jan has previously organized several important workshops, the "Learning for
 Locomotion" Workshop at Robotics: Science & Systems RSS 2005 conference and the NIPS 2006
 Workshop "Towards a New Reinforcement Learning?"
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