( Video, Slides) Joelle Pineau

Joelle Pineau


(Live) Jessica Zosa Forde

Jessica Zosa Forde

Brown University

( Video, Slides) Kirstie Whitaker

Kirstie Whitaker

Alan Turing Institute, Cambridge University

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 prioritize 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 broad range of topics covered at NeurIPS! The paper template is structured like a mini-tutorial on the pre-registration process to get you started quickly. Pre-registered papers will be published at the workshop. Authors will then 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). 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.

The deadline has passed. Accepted papers are available below.

Important Dates

1) Proposal phase: Selection of pre-registered papers for the NeurIPS Workshop
9th Oct 2020 Paper submission (authors)
17th Oct 2020 Reviews due (reviewers)
19th Oct 2020 Reviews available to authors
22nd Oct 2020 Rebuttal due (authors)
27th Oct 2020 Final reviews due (reviewers)
31st Oct 2020 Notification of decisions
27th Nov 2020 Camera ready submission (authors)
11th Dec 2020 NeurIPS 2020 workshop day
2) Results phase: Selection of results papers for PMLR
7th May 2021 Paper submission (authors)
7th June 2021 Reviews due (reviewers)
14th June 2021 Notification of decisions
28th June 2021 Camera ready submission (authors)
8th July 2021 PMLR volume published: http://proceedings.mlr.press/v148/

Accepted proposals

Playlist of all 1-minute preview videos

ID Authors Title Proposal Video Poster Results
5 Kexue Fu, Xiaoyuan Luo, Manning Wang Point Cloud Overlapping Region Co-Segmentation Network Proposal Video Poster Results
7 Udo Schlegel, Daniela Oelke, Daniel Keim, Mennatallah El-Assady An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks Proposal Video Poster
17 Akshay L Chandra, Sai Vikas Desai, Chaitanya Devaguptapu, Vineeth N Balasubramanian On Initial Pools for Deep Active Learning Proposal Video Poster Results
19 Liu Yuezhang, Bo Li, Qifeng Chen Evaluating Adversarial Robustness in Simulated Cerebellum Proposal Video Poster Results
21 XueHao Gao, Yang Yang, Shaoyi Du Contrastive Self-Supervised Learning for Skeleton Action Recognition Proposal Video Oral Poster Results
26 Ayush Jaiswal, Yue Wu, Pradeep Natarajan, Prem Natarajan Keypoints-aware Object Detection Proposal Video Poster Results
27 Cade Gordon, Natalie Parde Latent Neural Differential Equations for Video Generation Proposal Video Poster Results
28 Robert Vandermeulen, Rene Saitenmacher, Alexander Ritchie A Proposal for Supervised Density Estimation Proposal Video Poster
31 Eimear O' Sullivan, Stefanos Zafeiriou PCA Retargeting: Encoding Linear Shape Models as Convolutional Mesh Autoencoders Proposal Video Oral Poster Results
33 Rasmus Palm, Elias Najarro, Sebastian Risi Testing the Genomic Bottleneck Hypothesis in Hebbian Meta-Learning Proposal Video Oral Poster Results
36 Rodrigo Alves, Antoine Ledent, Renato Assunção, Marius Kloft An Empirical Study of the Discreteness Prior in Low-Rank Matrix Completion Proposal Video Poster Results
38 Elena Burceanu SFTrack++: A Fast Learnable Spectral Segmentation Approach for Space-Time Consistent Tracking Proposal Video Poster Results
39 Rishika Bhagwatkar, Khurshed Fitter, Saketh Bachu, Akshay Kulkarni, Shital Chiddarwar Paying Attention to Video Generation Proposal Video Poster Results
40 Chen Li, Xutan Peng, Hao Peng, Jianxin Li, Lihong Wang, Philip Yu, TextSGCN: Document-Level Graph Topology Refinement for Text Classification Proposal Video Poster
41 Chase Dowling, Ted Fujimoto, Nathan Hodas Policy Convergence Under the Influence of Antagonistic Agents in Markov Games Proposal Video Oral Poster
42 Arnout Devos ,Yatin Dandi Model-Agnostic Learning to Meta-Learn Proposal Video Poster Results
44 Carianne Martinez, Adam Brink, David Najera-Flores, D. Dane Quinn, Eleni Chatzi, Stephanie Forrest, Confronting Domain Shift in Trained Neural Networks Proposal Video Oral Poster Results
45 Joao Monteiro, Xavier Gibert, Jianqiao Feng, Vincent Dumoulin, Dar-Shyang Lee Domain Conditional Predictors for Domain Adaptation Proposal Video Poster Results
47 Tanner Bohn, Xinyu Yun, Charles Ling Towards a Unified Lifelong Learning Framework Proposal Video Poster Results
48 Hamid Eghbal-zadeh, Florian Henkel, Gerhard Widmer Context-Adaptive Reinforcement Learning using Unsupervised Learning of Context Variables Proposal Video Poster Results
50 Aneesh Dahiya, Adrian Spurr, Otmar Hilliges Exploring self-supervised learning techniques for hand pose estimation Proposal Video Poster Results
55 Sebastian Stabinger, David Peer, Antonio Rodriguez-Sanchez Training of Feedforward Networks Fails on a Simple Parity-Task Proposal Supmat Video Poster
56 Pablo Barros, Ana Tanevska, Ozge Nilay Yalcin, Alessandra Sciutti Incorporating Rivalry in Reinforcement Learning for a Competitive Game Proposal Video Poster
57 Steffen Schneider, Shubham Krishna, Luisa Eck, Wieland Brendel, Mackenzie Mathis, Matthias Bethge Generalized Invariant Risk Minimization: relating adaptation and invariant representation learning Proposal Supmat Video Poster
58 Alex Lewandowski Generalization Across Space and Time in Reinforcement Learning Proposal Video Poster
59 Prabhu Pradhan, Ruchit Rawal, Gopi Kishan Rendezvous between Robustness and Dataset Bias: An empirical study Proposal Video Poster
60 Miles Cranmer, Peter Melchior, Brian Nord Unsupervised Resource Allocation with Graph Neural Networks Proposal Video Oral Poster Results
62 Swaroop Mishra, Anjana Arunkumar, Bhavdeep Sachdeva Is High Quality Data All You Need? Proposal Video Poster
67 Yi-Fan Li, Yang Gao, Yu Lin, Zhuoyi Wang, Latifur Khan Time Series Forecasting Using a Unified Spatial-Temporal Graph Convolutional Network Proposal Supmat Video Poster
69 Norman Tasfi, Eder Santana, Miriam Capretz Policy Agnostic Successor Features Proposal Video Poster
71 Owen Lockwood, Mei Si Playing Atari with Hybrid Quantum-Classical Reinforcement Learning Proposal Video Poster Results
76 Ajinkya Mulay, Ayush Manish Agrawal, Tushar Semwal FedPerf: A Practitioners' Guide to Performance of Federated Learning Algorithms Proposal Video Oral Poster Results
77 Harshvardhan Sikka, Atharva Tendle, Amr Kayid Multimodal Modular Meta-Learning Proposal Video Poster
79 Philipp Benz, Chaoning Zhang, Adil Karjauv, In So Kweon Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy Proposal Video Poster Results
81 Ruizhe Li, Xutan Peng, Chenghua Lin, Frank Guerin, Wenge Rong On the low-density latent regions of VAE-based language models Proposal Video Oral Poster Results
82 Pratyush Kumar, Aishwarya Praveen Das, Debayan Gupta Differential Euler: Designing a Neural Network approximator to solve the Chaotic Three Body Problem Proposal Video Poster
83 Jiaqi Fan, Junxin Huang, Xiaochuan Yu, Chao He Data Subset Selection for Object Detection Proposal Video Poster
85 Meenakshi Sarkar, Debasish Ghose Decomposing camera and object motion for an improved Video Sequence Prediction Proposal Video Poster Results



João F. Henriques

João F. Henriques

University of Oxford

Samuel Albanie

Samuel Albanie

University of Oxford

Michela Paganini

Michela Paganini

Facebook AI Research

Gül Varol


University of Oxford


Many thanks to all the reviewers for their help!

Minttu Alakuijala
Yuki Asano
Max Bain
Fabien Baradel
Eloïse Berthier
Raphaël Berthier
Alberto Bietti
Hakan Bilen
Tolga Birdal
Oumayma Bounou
Margaux Bregere
Andrew Brown
Andrei Bursuc
Lénaïc Chizat
Jesse Dodge
Yuming Du
Christophe Dupuy
Sebastien Ehrhardt
Valentin Gabeur
Andrew Gambardella
Aude Genevay
Pascal Germain
Adam Golinski
Stuart Golodetz
Oliver Groth
Tom Gunter
Kai Han
Tengda Han
Yana Hasson
Eldar Insafutdinov
Ahmet Iscen
Xu Ji
Vicky Kalogeiton
A. Sophia Koepke
Viveka Kulharia
Valdimar Steinar Ericsson Laenen
Zihang Lai
Iro Laina
Shuda Li
Roxane Licandro
Erika Lu
Robert McCraith
Eric Metodiev
Grégoire Mialon
Liliane Momeni
Arsha Nagrani
Nantas Nardelli
Lukas Neumann
Maxime Oquab
Anuj Pahuja
Alexander Pashevich
Mandela Patrick
Loucas Pillaud Vivien
Ameya Prabhu
Tom Rainforth
Ignacio Rocco
Manon Romain
Vincent Roulet
Christian Rupprecht
Levent Sagun
Lukas Schäfer
Li Shen
Oriane Siméoni
Umut Simsekli
Robin Strudel
Adrien Taylor
Damien Teney
James Thornton
Jack Valmadre
Bichen Wu
Shangzhe Wu
Weidi Xie
Charig Yang
Chuhan Zhang