Speakers
Transactions on Machine Learning Research: A New Open Journal for Machine Learning
| Time (UTC) | Session | Duration |
|---|---|---|
| 12:00 | Opening Remarks | 0:10 |
| 12:10 | Sarahanne Field (Invited Talk) Preregistration: Introduction and Application to ML | 0:30 |
| 12:40 | Oral Session 1 PCA Retargeting: Encoding Linear Shape Models as Convolutional Mesh Autoencoders (Eimear O'Sullivan) | 0:20 |
| 13:00 | Spotlights 1 (5 x 3 min) | 0:20 |
| 13:20 | Oral Session 2 Unsupervised Resource Allocation with Graph Neural Networks (Miles Cranmer) | 0:20 |
| 13:40 | Break | 0:30 |
| 14:10 | Dima Damen (Invited Talk) Defending the Undefendable - Why I support peer reviewing? | 0:30 |
| 14:40 | Hugo Larochelle (Invited Talk) Transactions on Machine Learning Research: A New Open Journal for Machine Learning | 0:30 |
| 15:10 | Spotlights 2 (5 x 3 min) | 0:20 |
| 15:30 | Poster Session | 1:00 |
| 16:30 | Break | 0:30 |
| 17:00 | Paul Smaldino (Invited Talk) Preregistration: A Reasonably Good Idea In A Time of Crisis | 0:30 |
| 17:30 | Oral Session 3 Confronting Domain Shift in Trained Neural Networks (Carianne Martinez) | 0:20 |
| 17:50 | 2020 Authors' Experience (Discussion Panel) | 0:15 |
| 18:05 | Open Discussion | 1:00 |
| 19:05 | Closing Remarks | 0:05 |
Including a playlist of all 3-minute spotlight videos.
| ID | Authors | Title | Proposal | Video | Poster |
| 3 | Shubhaankar Gupta, Thomas P. O’Connell, Bernhard Egger | Beyond Flatland: Pre-training with a Strong 3D Inductive Bias | Proposal | Video | Poster |
| 6 | Mikolaj Czerkawski, Javier Cardona, Robert Atkinson, Craig Michie, Ivan Andonovic, Carmine Clemente, Christos Tachtatzis | Neural Weight Step Video Compression | Proposal | Video | Poster |
| 8 | Hamid Eghbal-zadeh, Gerhard Widmer | How Much is an Augmented Sample Worth? | Proposal | Video | Poster |
| 10 | Steven Lang, Martin Mundt, Fabrizio Ventola, Robert Peharz, Kristian Kersting | Elevating Perceptual Sample Quality in Probabilistic Circuits through Differentiable Sampling | Proposal | Video | Poster |
| 11 | Rohit Lal, Arihant Gaur, Aadhithya Iyer, Muhammed Abdullah Shaikh, Ritik Agrawal, Shital Chiddarwar | Open-Set Multi-Source Multi-Target Domain Adaptation | Proposal Supmat | Video | Poster |
| 15 | Sebastian Palacio, Federico Raue, Tushar Karayil, Jörn Hees, Andreas Dengel | IteROAR: Quantifying the Interpretation of Feature Importance Methods | Proposal | Video | Poster |
| 18 | Kshitij Ambilduke, Aneesh Shetye, Diksha Bagade, Rishika Bhagwatkar, Khurshed Fitter, Prasad Vagdargi, Shital Chiddarwar | Enhancing Context Through Contrast | Proposal | Video | Poster |
| 19 | Shuyang Li, Huanru Henry Mao, Julian McAuley | Variable Bitrate Discrete Neural Representations via Causal Self-Attention | Proposal | Video | Poster |
| 26 | Pierre Thodoroff, Wenyu Li, Neil D. Lawrence | Benchmarking Real-Time Reinforcement Learning | Proposal | Video | Poster |
| 29 | Vaasudev Narayanan, Aniket Anand Deshmukh, Urun Dogan, Vineeth N Balasubramaniam | On Challenges in Unsupervised Domain Generalization | Proposal | Video | Poster |
What is pre-registration and how does it improve peer-review? Benchmarks on popular datasets have played a key role in the considerable measurable progress that machine learning has made in the last few years. But reviewers can be tempted to prioritise incremental improvements in benchmarks to the detriment of other scientific criteria, destroying many good ideas in their infancy. Authors can also feel obligated to make orthogonal improvements in order to “beat the state-of-the-art”, making the main contribution hard to assess.
Pre-registration changes the incentives by reviewing and accepting a paper before experiments are conducted. The emphasis of peer-review will be on whether the experiment plan can adequately prove or disprove one (or more) hypotheses. Some results will be negative, and this is welcomed. This way, good ideas that do not work will get published, instead of filed away and wastefully replicated many times by different groups. Finally, the clear separation between hypothesizing and confirmation (absent in the current review model) will raise the statistical significance of the results.
Call for Papers: We are inviting submissions on the range of topics covered at NeurIPS! Pre-registered papers will be published at the workshop, which is non-archival. After NeurIPS, authors will have the opportunity to submit the results paper to the Proceedings of Machine Learning Research (PMLR), a sister publication to the Journal of Machine Learning Research (JMLR). (you can find last year's proceedings here). The review process for this second stage will aim to ensure that the authors have performed a good-faith attempt to complete the experiments described in their proposal paper.
| → There are two phases. The first one involves the presentation of paper proposals and will conclude with NeurIPS 2021 workshop day. The experiment phase will start after the acceptance of the proposals and will continue after the day of the workshop. |
| → The deadline for the proposal is September the 19th. The deadline for the results is |
| → The proposal is non-archival and will be included in the workshop proceedings. The full papers, formed as the collation of proposal and results, will each published as a journal in a PMLR volume. |
| → Please read our tutorial before submitting, which you can find here. The paper structure and general rationale is different to what you may be used to. Even if the proposals must not include experimental results, it is important to carefully design and describe the experimental protocol, with the aim of eventually obtaining conclusive results. |
| → You can submit here via CMT. For your proposals, please use our modified NeurIPS template. |
| → For the proposal, the maximum length is five pages (references excluded). For the full papers there is not a strict limit, although we recommend to limit the experiments to a maximum extra four pages with respect to the proposal. |
| → For inspiration, have a look at the other sections of this website, which provide further information. Moreover, you can find all the videos and proposals of last year edition here, and the PMLR volume with the full papers from 2020 workshop here. |
1) Proposal phase: |
Selection of pre-registered papers for the NeurIPS Workshop |
| 19th Sept 2021 | Paper submission (authors) |
| 20th Sept to 27th Sept 2021 | Review period (reviewers) |
| 1st Oct to 8th Oct 2021 | Rebuttal period (authors) |
| 22nd Oct 2021 | Notification of decisions |
| 1st Dec 2021 | Camera ready submission (authors) |
| 13th Dec 2021 | NeurIPS 2021 workshop day | 2) Results phase: |
Selection of results papers for PMLR journal |
| |
Full paper (with results) submission (authors) |
Many thanks to all the reviewers for their help!
This is the third edition of the workshop. Below you can find previous years' pages.
In particular, here you can find the PMLR proceedings of the final papers (proposal+experiments) from last year's edition.
