Anna Goldenberg, Ndapa Nakashole, Sanmi Koyejo, Tristan Naumann, NeurIPS Workshop Co-Chairs
We’re excited to announce the NeurIPS 2021 workshops! We received 131 total submissions — roughly on par with last year. From this great batch of submissions, we accepted 60 workshops. We wish we could have accepted many more of these high-quality proposals, but could not due to technical constraints with our simultaneous verification and live-streaming. This is the second year that the workshop selection process had to be so selective, and we expect that many of the excellent proposals that we could not accept will be revised, resubmitted, and presented at other ML conferences.
Before launching into this, we want to thank all the people who put in the effort to propose workshops. As Workshop Chairs, all we can do is guide the development of these proposals. The work is done by the organizers. So thank you!
Innovations This Year: Submission format and representative Workshop reviewers
The first thing that the four of us did—and perhaps the most important component of our role—was to rethink and substantially revise the workshop submission template. The new submission template was designed to improve the ease of submission and reduce the variance in the workshop reviews by standardizing the length of the workshop proposals and clarifying the content relevant for reviewer evaluation. Specifically, we limited the proposal to two pages of content and two pages of organizer information, along with unlimited references. Some specific details we requested included:
- A list of invited speakers, if applicable, with an indication of which ones have already agreed and which are tentative.
- An account of the efforts made to ensure demographic diversity of the organizers and speakers and an account of any efforts to include diverse participants.
- An estimate of the number of attendees.
- A description of special requirements and technical needs.
- A very brief advertisement or tagline for the workshop, up to 140 characters, that highlights key information for prospective attendees to know, and which would be suitable to be put onto a web-based survey.
- A URL for the workshop website.
- The two pages (or fewer) for information about organizers must include: The names, affiliations, and email addresses of the organizers, with one-paragraph statements of their research interests, areas of expertise, and experience in organizing workshops and related events. Finally, a list of Programme Committee members, with an indication of which members have already agreed, anticipating that no one is committed to reviewing more than 3 papers.
A second innovation compared to previous years was the choice to have a larger and more representative program committee. In the past, the program committee has included only senior members of the NeurIPS community. This year, in an effort to follow the same diversity guidelines as we asked from the proposals, we invited a broad subset of workshop proposers to consider submitting workshop reviews. Our selection was careful to avoid any conflict of interest.
In making our selections, we asked the reviewers to closely follow our Guidance for Workshop Proposals, which was also shared with the proposers. Workshop proposals must be reviewed somewhat differently from academic papers, and we asked the reviewers to consider both scientific merits and broader impacts in their assessments. We recognize that workshop reviews are perhaps more subjective than academic paper reviews. Following the practice of previous years, we will not be releasing the reviews directly to the proposers. However, following the precedent of last year, we released a short meta-review for each proposal — aiming to highlight what could be improved, or explaining how the proposal was perceived by the reviewers.
Individual evaluations of proposals by reviewers were important to the decision process, but they were not the only consideration for acceptance. For example, we also strived for a good balance between research areas and between application and theory. Because interest in research areas is not uniform, some areas were more competitive than others. For example, there were many strong federated learning proposals. We also received many submissions on important current topics such as reinforcement learning, privacy, fairness, causality, and needless to say various topics on deep learning. We attempted some balance of topic areas to cover both mainstays and emerging topics.
It is also worth noting that we saw many of the pitfalls outlined in previous years. This included leaning too heavily on past success, unconfirmed or irrelevant speakers, insufficient time for discussion, going too big and broad, and diversity lip service.
We learned a lot from the 2020 NeurIPS workshop chairs about the constraints and opportunities of virtual workshops, e.g. (and perhaps most importantly), encouraging the use of a shared technology platform. Our chosen technology platforms allow us to tackle technology issues more effectively, pool resources across workshops, and simplify the user experience for workshop attendees.
The next step is your contributions! Many workshops have begun soliciting submissions, many using our suggested submission date of September 17. We typically let each workshop advertise its own call for papers if they plan to include workshop papers. This year, we are imposing a few deadlines because of the choice to use a common platform for the talks, which appeared to be necessary for hosting 60 events at the same time in a smooth manner. More technical and contextual information is coming soon!
NeurIPS 2021 Accepted Workshops
On to the best part: the preliminary list of accepted workshops for 2021!
- 2nd Workshop on Self-Supervised Learning: Theory and Practice
- Advances in Programming Languages and Neurosymbolic Systems (AIPLANS)
- Closing the Gap between Academia and Industry in Federated Learning: Challenges on Privacy, Fairness, Robustness, Personalization and Data Ownership
- Bayesian Deep Learning
- Math AI for Education (MATHAI4ED): Bridging the Gap Between Research and Smart Education
- AI for Science: Mind the Gaps
- Differentiable Programming Workshop
- Second Workshop on Quantum Tensor Networks in Machine Learning
- The Symbiosis of Deep Learning and Differential Equations
- Deep Generative Models and Downstream Applications
- Medical Imaging meets NeurIPS
- OPT 2021: Optimization for Machine Learning
- Machine Learning for Autonomous Driving
- 4th Robot Learning Workshop: Self-Supervised and Lifelong Learning
- Workshop on Human and Machine Decisions
- Causal Inference Challenges in Sequential DecisionMaking: Bridging Theory and Practice
- Safe and Robust Control of Uncertain Systems
- Your model is wrong: Robustness and misspecification in probabilistic modeling
- Physical Reasoning and Inductive Biases for the Real World
- Efficient Natural Language and Speech Processing (Models, Training, and Inference)
- Bridging the Gap: from Machine Learning Research to Clinical Practice
- Machine Learning Meets Econometrics (MLECON)
- Algorithmic Fairness through the lens of Causality and Robustness
- ImageNet: Past, Present, and Future.
- Optimal Transport and Machine Learning
- Machine Learning in Public Health
- Cooperative AI
- Workshop on Deep Learning and Inverse Problems
- Meaning in Context: Pragmatic Communication in Humans and Machines
- ML For Systems
- Human-Centered AI
- Learning in Presence of Strategic Behavior
- Decision-Making and Learning with Strategic Feedback
- Metacognition in the Age of AI: Challenges and Opportunities
- AI for Credible Elections: A Call to Action
- Shared Visual Representations in Human and Machine Intelligence
- The Political Economy of Reinforcement Learning Systems (PERLS)
- Deep Reinforcement Learning
- Privacy in Machine Learning (PriML) 2021
- Machine Learning for Creativity and Design
- Databases and AI (DBAI)
- Workshop on Pre-registration in ML
- Machine learning from ground truth: New medical imaging datasets for unsolved medical problems.
- CtrlGen: Controllable Generative Modeling in Language and Vision
- Out-of-distribution generalization and adaptation in natural and artificial intelligence
- eXplainable AI approaches for debugging and diagnosis
- Data Centric AI
- Ecological Theory of Reinforcement Learning: How Does Task Design Influence Agent Learning?
- Third Workshop on AI for Humanitarian Assistance and Disaster Response
- Distribution Shifts in Real-World Applications
- I (Still) Can’t Believe It’s Not Better
- Causal Inference & Machine Learning: Why now?
- Machine Learning and the Physical Sciences
- Machine Learning in Structural Biology
- Workshop on Meta-Learning
- Learning Meaningful Representations of Life (LMRL)
- Deployable Decision Making in Embodied Systems (DDM)
- Offline Reinforcement Learning
- Tackling Climate Change with Machine Learning
- Machine Learning for the Developing World (ML4D): Global Challenges