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Second session for the competition program at NeurIPS2020.
Machine learning competitions have grown in popularity and impact over the last decade, emerging as an effective means to advance the state of the art by posing well-structured, relevant, and challenging problems to the community at large. Motivated by a reward or merely the satisfaction of seeing their machine learning algorithm reach the top of a leaderboard, practitioners innovate, improve, and tune their approach before evaluating on a held-out dataset or environment. The competition track of NeurIPS has matured in 2020, its fourth year, with a considerable increase in both the number of challenges and the diversity of domains and topics. A total of 16 competitions are featured this year as part of the track, with 8 competitions associated to each of the two days. The list of competitions that ar part of the program are available here:
https://neurips.cc/Conferences/2020/CompetitionTrack
Sat 8:00 a.m. - 8:05 a.m.
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Introducing EfficientQA: Open domain question answering with memory constraints as a testbed for language understanding and knowledge representations
(
Live oral presentation
)
|
Tom Kwiatkowski 🔗 |
Sat 8:05 a.m. - 8:25 a.m.
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Track winner presentations:
(
Live oral presentation
)
|
Tom Kwiatkowski 🔗 |
Sat 8:25 a.m. - 8:45 a.m.
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Showdown against trivia experts
(
Live competition
)
Five top human teams of trivia experts took on the competition’s baseline systems for the opportunity to take on the computer systems in each of the competition’s divisions. The team of humans will compete against the computer on thirty questions from the test set. We will present highlights from the preliminary competition as well as the final showdown between computer systems and the human teams. |
Jordan Boyd-Graber 🔗 |
Sat 9:00 a.m. - 9:06 a.m.
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Predicting Generalization in Deep Learning (PGDL): Opening remark
(
Intro
)
|
Yiding Jiang 🔗 |
Sat 9:06 a.m. - 9:16 a.m.
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Keynote speech: Sanjeev Arora (PGDL)
(
Oral presentation
)
|
Sanjeev Arora · Yiding Jiang 🔗 |
Sat 9:16 a.m. - 9:17 a.m.
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Introduction to winning team (PGDL)
(
Intro
)
|
Yiding Jiang 🔗 |
Sat 9:17 a.m. - 9:27 a.m.
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Winning team presentation: On Representations and Generalization (PGDL)
(
Oral presentation
)
|
Yiding Jiang · Parth Natekar · Manik Sharma 🔗 |
Sat 9:27 a.m. - 9:28 a.m.
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Introduction to Runner up 1 (PGDL)
(
Intro
)
|
Yiding Jiang 🔗 |
Sat 9:28 a.m. - 9:33 a.m.
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Runner up presentation: Robustness to Augmentations as a Generalization Metric (PGDL)
(
Oral presentation
)
SlidesLive Video » |
Sumukh K Aithal 🔗 |
Sat 9:33 a.m. - 9:34 a.m.
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Introduction to Runner up 2 (PGDL)
(
Intro
)
|
Yiding Jiang 🔗 |
Sat 9:34 a.m. - 9:39 a.m.
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Runner up presentation: Ranking generalization via smoothness of latent graphs (PGDL)
(
Oral presentation
)
SlidesLive Video » |
Carlos Lassance 🔗 |
Sat 9:39 a.m. - 9:44 a.m.
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Closing remark (PGDL)
(
Outro
)
|
Yiding Jiang 🔗 |
Sat 10:00 a.m. - 10:15 a.m.
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Openning and design of the INTERPRET challenge @ NeurIPS2020
(
Oral presentation
)
|
Wei Zhan 🔗 |
Sat 10:15 a.m. - 10:35 a.m.
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Winner talks of INTERPRET challenge
(
Oral presentation
)
|
Hengbo Ma 🔗 |
Sat 10:35 a.m. - 10:45 a.m.
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Analysis, research opportunities and closing of INTERPRET challenge
(
Oral presentation
)
|
Liting Sun 🔗 |
Sat 11:00 a.m. - 11:05 a.m.
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NLC2CMD Competition Organizers: Introduction, Problem Description, CLAI
(
Oral presentation
)
SlidesLive Video » |
Mayank Agarwal 🔗 |
Sat 11:05 a.m. - 11:15 a.m.
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NLC2CMD Competition Keynote: Tellina
(
Oral presentation
)
|
Victoria Lin · Mayank Agarwal · Tathagata Chakraborti 🔗 |
Sat 11:15 a.m. - 11:20 a.m.
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NLC2CMD Competition Organizers: Metrics, Data, Tracks
(
Live oral presentation
)
SlidesLive Video » |
Mayank Agarwal 🔗 |
Sat 11:20 a.m. - 11:22 a.m.
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NLC2CMD Competition Participant Team: AINixClaiSimple
(
Oral presentation
)
SlidesLive Video » |
David Gros 🔗 |
Sat 11:22 a.m. - 11:24 a.m.
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NLC2CMD Competition Participant Team: coinse-team
(
Oral presentation
)
SlidesLive Video » |
Juyeon Yoon 🔗 |
Sat 11:24 a.m. - 11:26 a.m.
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NLC2CMD Competition Participant Team: AICore
(
Oral presentation
)
SlidesLive Video » |
Kangwook Lee 🔗 |
Sat 11:26 a.m. - 11:28 a.m.
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NLC2CMD Competition Participant Team: magnum
(
Oral presentation
)
SlidesLive Video » |
Quchen Fu 🔗 |
Sat 11:28 a.m. - 11:30 a.m.
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NLC2CMD Competition Participant Team: Hubris
(
Oral presentation
)
SlidesLive Video » |
Jaron Maene 🔗 |
Sat 11:30 a.m. - 11:32 a.m.
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NLC2CMD Competition Participant Team: jb
(
Oral presentation
)
SlidesLive Video » |
Denis Litvinov 🔗 |
Sat 11:32 a.m. - 11:45 a.m.
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NLC2CMD Competition Organizers: Results (Live)
(
Outro
)
|
Kartik Talamadupula 🔗 |
Sat 2:00 p.m. - 2:03 p.m.
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Introduction to AIDO
(
Intro
)
|
Liam Paull 🔗 |
Sat 2:03 p.m. - 2:05 p.m.
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Short Scientific Talk (AIDO)
(
Oral presentation
)
|
Luigi Di Lillo 🔗 |
Sat 2:05 p.m. - 2:13 p.m.
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Advanced Perception League
(
Oral presentation
)
|
Liam Paull 🔗 |
Sat 2:13 p.m. - 2:19 p.m.
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Intro to Urban League (includes highlights from semifinals)
(
Intro
)
|
Liam Paull 🔗 |
Sat 2:19 p.m. - 2:34 p.m.
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Live robot competition (LF, LFP, lFVM)
(
Live competition
)
|
Liam Paull 🔗 |
Sat 2:34 p.m. - 2:40 p.m.
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Interviews with winners
(
Panel discussion
)
|
Liam Paull 🔗 |
Sat 2:40 p.m. - 2:43 p.m.
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Conclusions and Wrap up
(
Outro
)
|
Liam Paull 🔗 |
Sat 3:00 p.m. - 3:05 p.m.
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Introduction - Flatland
(
Intro
)
|
Sharada Mohanty 🔗 |
Sat 3:05 p.m. - 3:15 p.m.
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Flatland Competition Design & Results
(
Oral presentation
)
SlidesLive Video » |
Sharada Mohanty 🔗 |
Sat 3:15 p.m. - 3:19 p.m.
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Winner Talks : Team An Old Driver
(
Oral presentation
)
SlidesLive Video » |
Sharada Mohanty 🔗 |
Sat 3:19 p.m. - 3:23 p.m.
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Winner Talks : Team JBR_HSE
(
Oral presentation
)
SlidesLive Video » |
Sharada Mohanty 🔗 |
Sat 3:23 p.m. - 3:27 p.m.
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Winner Talks : Team ai-team-flatland
(
Oral presentation
)
SlidesLive Video » |
Sharada Mohanty 🔗 |
Sat 3:27 p.m. - 3:42 p.m.
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"Real world applications of Flatland" : Panel Discussion with SBB, DeutschBahn, SNCF
(
Oral presentation
)
SlidesLive Video » |
Sharada Mohanty 🔗 |
Sat 3:42 p.m. - 3:45 p.m.
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Concluding Remarks
(
Outro
)
|
Sharada Mohanty 🔗 |
Sat 4:00 p.m. - 4:03 p.m.
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Introduction - Procgen
(
Intro
)
|
Sharada Mohanty 🔗 |
Sat 4:03 p.m. - 4:12 p.m.
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Introduction to the Procgen Benchmark
(
Oral presentation
)
SlidesLive Video » |
Karl Cobbe 🔗 |
Sat 4:12 p.m. - 4:20 p.m.
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NeurIPS 2020 Procgen Challenge Design
(
Oral presentation
)
SlidesLive Video » |
Sharada Mohanty 🔗 |
Sat 4:20 p.m. - 4:25 p.m.
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Winner Announcements & Analysis of top submissions
(
Oral presentation
)
SlidesLive Video » |
Sharada Mohanty 🔗 |
Sat 4:25 p.m. - 4:44 p.m.
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Sample Efficiency & Generalization in RL : An assortment of tricks (talks by top participants)
(
Oral presentation
)
SlidesLive Video » |
Sharada Mohanty 🔗 |
Sat 4:44 p.m. - 4:45 p.m.
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Concluding Remarks
(
Outro
)
|
Sharada Mohanty 🔗 |
Sat 5:00 p.m. - 5:45 p.m.
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Introduction and results of the 2020 MineRL Competition
(
Live oral presentation
)
|
William Guss · Stephanie Milani · Nicholay Topin 🔗 |
Author Information
Hugo Jair Escalante (INAOE)
Katja Hofmann (Microsoft Research)
Dr. Katja Hofmann is a Principal Researcher at the [Game Intelligence](http://aka.ms/gameintelligence/) group at [Microsoft Research Cambridge, UK](https://www.microsoft.com/en-us/research/lab/microsoft-research-cambridge/). There, she leads a research team that focuses on reinforcement learning with applications in modern video games. She and her team strongly believe that modern video games will drive a transformation of how we interact with AI technology. One of the projects developed by her team is [Project Malmo](https://www.microsoft.com/en-us/research/project/project-malmo/), which uses the popular game Minecraft as an experimentation platform for developing intelligent technology. Katja's long-term goal is to develop AI systems that learn to collaborate with people, to empower their users and help solve complex real-world problems. Before joining Microsoft Research, Katja completed her PhD in Computer Science as part of the [ILPS](https://ilps.science.uva.nl/) group at the [University of Amsterdam](https://www.uva.nl/en). She worked with Maarten de Rijke and Shimon Whiteson on interactive machine learning algorithms for search engines.
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