Hsuan-Tien Lin, Maria Florina Balcan, Raia Hadsell and Marc’Aurelio Ranzato

NeurIPS 2020 Program Chairs

In this blog post, we are excited to announce the various awards that are presented at NeurIPS 2020 and to share information about the selection processes for these awards.

NeurIPS 2020 Best Paper Awards

The winners of the NeurIPS 2020 Best Paper Awards are:

  • No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium by Andrea Celli (Politecnico di Milano), Alberto Marchesi (Politecnico di Milano), Gabriele Farina (Carnegie Mellon University), and Nicola Gatti (Politecnico di Milano). This paper will be presented on Tuesday, December 8th at 6:00 AM PST in the Learning Theory track.

Selection process. The NeurIPS 2020 best paper awards were selected by a committee that included Nicolò Cesa-Bianchi, Jennifer Dy, Surya Ganguli, Masashi Sugiyama, and Laurens van der Maaten, who shared with us the following details about the selection process.

In selecting winning papers, the committee used the following review criteria: Does the paper have the potential to endure? Does it provide new (and hopefully deep) insights? Is it creative and unexpected? Might it change the way people think in the future? Is it rigorous and elegant but does not over-claim its significance? Is it scientific and reproducible? Does it accurately describe the broader impact of the research?

To select the winners of the NeurIPS Best Paper Awards, the award committee went through a rigorous two-stage selection process:

  • In the first stage of the process, the 30 NeurIPS submissions with the highest review scores were read by two committee members. Committee members also read the corresponding paper reviews and rebuttal. Based on this investigation, the committee selected nine papers that stood out according to the reviewing criteria.

In particular, the committee provided the following motivation for selecting three winning papers:

  • No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium. Correlated equilibria (CE) are easy to compute and can attain a social welfare that is much higher than that of the better-known Nash equilibria. In normal form games, a surprising feature of CE is that they can be found by simple and decentralized algorithms minimizing a specific notion of regret (the so-called internal regret). This paper shows the existence of such regret-minimizing algorithms that converge to CE in a much larger class of games: namely, the extensive-form (or tree-form) games. This result solves a long-standing open problem at the interface of game theory, computer science, and economics and can have substantial impact on games that involve a mediator, for example, on efficient traffic routing via navigation apps.

Test of Time Award

We also continued the tradition of selecting a paper published about a decade ago at NeurIPS and that was deemed to have had a particularly significant and lasting impact on our community. We are delighted to announce that the winner of the NeurIPS 2020 test of time award is HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent published in NeurIPS 2011 and authored by Feng Niu, Benjamin Recht, Christopher Re, and Stephen Wright.

This paper was the first to show how to parallelize the ubiquitously used Stochastic Gradient Descent algorithm without any locking mechanism while achieving strong performance guarantees. At the time, several researchers proposed ways to parallelize SGD, but they all required memory locking and synchronization across the different workers. This paper proposed a simple strategy for sparse problems called Hogwild!: have each worker concurrently run SGD on a different subset of the data and perform fully asynchronous updates in the shared memory hosting the parameters of the model. Through both theory and experiments, they demonstrated that Hogwild! achieves a near linear speedup with the number of processors on data satisfying appropriate sparsity conditions.

You can find more about the paper and its impact by attending the Test of Time talk on Wednesday December 9th at 6:00 AM PST in the Optimization track.

Selection process. We identified a list of 12 papers published at NeurIPS about a decade ago (NeurIPS 2009, NeurIPS 2010, NeurIPS 2011). These were the papers from these NeurIPS editions with the highest numbers of citations since their publication. We also collected data about the recent citations counts for each of these papers by aggregating citations that these papers received in the past two years at NeurIPS, ICML and ICLR. We then asked the whole senior program committee (64 SACs) to vote on up to three of these papers to help us pick an impactful paper about which the whole senior program committee was enthusiastic.

Reviewer Awards

Finally, but equally importantly, we again selected reviewer award winners. We selected the top 10% of reviewers, that is 730 reviewers, to receive this award. We made the selection based on the average rating of reviews they entered in the system (where the ratings were provided by the area chairs). We thank all these reviewers for their outstanding work and as a small token of appreciation they were given free registration.

Congratulations to all awardees for their great research or service contribution to our thriving community!

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