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December 6 - 14, 2021
Virtual
Conference
NeurIPS 2021

Overview

The Neural Information Processing Systems (NeurIPS) annual meeting fosters the exchange of research on neural information processing systems in their biological, technological, mathematical, and theoretical aspects. The core focus is peer-reviewed novel research which is presented and discussed in the general session, along with invited talks by leaders in their fields.

Sponsorship Details

Conference website
Related research
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Amazon program committee and board members

Accepted publications (partial list)

Workshops

NeurIPS 2021 Workshop on Queer in AI
Unknown date
Amazon organizer: Luca Soldaini

Website: https://sites.google.com/view/queer-in-ai/neurips-2021?authuser=0
NeurIPS 2021 Workshop on Learning in Presence of Strategic Behavior
December 13, 12:00 PM - 5:50 PM EST
Amazon speaker: Michael I. Jordan (Amazon Scholar)

Website: https://sites.google.com/view/strategicml/about
NeurIPS 2021 Workshop on Women in Machine Learning (WiML 2021)
December 10
Amazon organizer: Mariya Vasileva

Website: https://sites.google.com/view/wiml2021/home
NeurIPS 2021 Workshop on Efficient Natural Language and Speech Processing
December 13, 8:00 AM - 8:00 PM EST
Amazon organizers: Di Jin, Peyman Passban, Tanya Roosta, Ankit Chadha, Aashiq Muhamed, Iman Keivanloo

Website: https://neurips2021-nlp.github.io/
Communication-efficient federated learning for neural machine translation
Tanya Roosta, Peyman Passban, Ankit Chadha
2021
Conversational AI / Natural-language processing
Magic pyramid: Accelerating inference with early exiting and token pruning
Xuanli He, Iman Keivanloo, Yi Xu, Xiang He, Belinda Zeng, Santosh Rajagopalan, Trishul Chilimbi
2021
Machine learning
Towards continual entity learning in language models for conversational agents
Ravi Teja Gadde, Ivan Bulyko
2021
Conversational AI / Natural-language processing
Towards zero- and few-shot knowledge-seeking turn detection in task-oriented dialogue systems
Di Jin, Shuyang Gao, Seokhwan Kim, Yang Liu, Dilek Hakkani-Tür
2021
Related: Alexa AI team discusses NeurIPS workshop best paper award
Conversational AI / Natural-language processing
NeurIPS 2021 Workshop on Causal Inference & Machine Learning: Why now?
December 13, 9:30 AM - 6:00 PM EST
Amazon speaker: Victor Chernozhukov (Amazon Scholar)

Amazon organizer: Bernhard Schölkopf

Website: https://why21.causalai.net/
NeurIPS 2021 Workshop on Metacognition in the Age of AI: Challenges and Opportunities
December 13, 8:00 AM - 5:30 PM EST
Amazon speaker: Bernhard Schölkopf

Website: https://sites.google.com/view/metacogneurips2021/home
NeurIPS 2021 Workshop on Machine Learning meets Econometrics (MLECON)
December 13, 8:00 AM - 3:10 PM EST
Amazon speaker: Eric Tchetgen Tchetgen (Amazon Scholar)

Website: https://sites.google.com/view/mlecon2021/home
NeurIPS 2021 Workshop on Databases and AI (DBAI)
December 13, 8:50 AM - 5:45 PM EST
Website: https://dbai-workshop.github.io/
DP-KB: Data programming with knowledge bases improves transformer fine tuning for answer sentence selection
Nic Jedema, Thuy Vu, Manish Gupta, Alessandro Moschitti
2021
Conversational AI / Natural-language processing
NeurIPS 2021 Workshop on Safe and Robust Control of Uncertain Systems
December 13, 11:00 AM - 7:00 PM EST
Website: https://sites.google.com/view/safe-robust-control/home
Robust reinforcement learning for shifting dynamics during deployment
Samuel Stanton, Rasool Fakoor, Jonas Mueller, Andrew Gordon Wilson, Alex Smola
2021
Machine learning
NeurIPS 2021 Workshop on New Frontiers in Federated Learning: Privacy, Fairness, Robustness, Personalization and Data Ownership
December 13, 8:30 AM - 7:00 PM EST
Amazon organizer: Nghia Hoang, Anoop Deoras

Website: https://neurips2021workshopfl.github.io/NFFL-2021/index.html
NeurIPS 2021 Workshop on Human Centered AI (HCAI)
December 13
Website: https://sites.google.com/view/hcai-human-centered-ai-neurips/home
Interpreting voice assistant interaction quality from unprompted user feedback
Pragati Verma, Sudeeksha Murari
2021
Conversational AI / Natural-language processing
NeurIPS 2021 Workshop on Causal Sequential Decisions
December 14
Amazon speaker: Rui Song (Amazon Scholar)

Website: https://sites.google.com/view/causal-sequential-decisions/home
NeurIPS 2021 Workshop on Out-of-Distribution Generalization and Adaptation in Natural and Artificial Intelligence
December 14, 8:00 AM - 3:20 PM EST
Amazon speaker: Bernhard Schölkopf

Website: https://docs.neurodata.io/ood-workshop/committee
NeurIPS 2021 Workshop on Privacy in Machine Learning
December 14, 11:10 AM - 8:55 PM EST
Amazon speaker: Aaron Roth (Amazon Scholar)

Website: https://priml2021.github.io/
Reconstructing test labels from noisy loss scores
Abhinav Aggarwal, Shiva Kasiviswanathan, Zekun Xu, Oluwaseyi Feyisetan, Nathanael Teissier
2021
Security, privacy, and abuse prevention
NeurIPS 2021 Workshop on Learning and Decision-Making with Strategic Feedback (StratML'21)
December 14, 10:00 AM - 5:15 PM EST
Amazon organizer: Aaron Roth (Amazon Scholar)

Website:  https://www.strategic-ml.com/
NeurIPS 2021 Workshop on Data-Centric AI
December 14
Website: https://datacentricai.org/
Comparing data augmentation and annotation standardization to improve end-to-end spoken language understanding models
Leah Nicolich-Henkin, Taichi Nakatani, Zach Trozenski, Joel Whiteman, Nathan Susanj
2021
Conversational AI / Natural-language processing
A first look towards one-shot object detection with SPOT for data-efficient learning
Ria Chakraborty, Madhur Popli, Rachit Lamba, Rishi Verma
2021
Computer vision
NeurIPS 2021 Workshop on Distribution Shifts
December 13
Website: https://sites.google.com/view/distshift2021
Just mix once: Mixing samples with implicit group distribution
Giorgio Giannone, Serhii Havrylov, Jordan Massiah, Emine Yilmaz, Yunlong Jiao
2021
Computer vision
Gradient-matching coresets for continual learning
Lukas Balles, Giovanni Zappella, Cédric Archambeau
2021
Machine learning
NeurIPS 2021 Workshop on I (Still) Can't Believe It's Not Better
December 13
Website: https://i-cant-believe-its-not-better.github.io/neurips2021/
GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics
Ke Alexander Wang, Danielle Maddix Robinson, Yuyang (Bernie) Wang
2021
Machine learning
Language models as recommender systems: Evaluations and limitations
Yuhui Zhang, Hao Ding, Zeren Shui, Yifei Ma, James Zou, Anoop Deoras, Hao Wang
2021
Machine learning

Virtual Events

A conversation with AI luminaries
December 2, 1:00 PM - 2:00 PM EST
Michael I. Jordan, Michael Kearns, and Bernhard Schölkopf discuss the future of AI
Panelists
Michael Jordan
Amazon Scholar
Michael Kearns
Michael Kearns
Amazon Scholar
University of Pennsylvania
Bernhard
Bernhard Schölkopf
VP, Distinguished Scientist
Database & AI Leadership
Watch the replay
The challenge and promise of quantum computing
December 15, 1:00 PM - 2:15 PM EST
Join seven Amazon scientists, Scholars and Visiting Academics as they discuss the current and future challenges of quantum computing.
Panelists
Simone Severini
Director, Applied Science
Antia Lamas-Linares
Senior Manager, Applied Science
Earl Campbell
Senior Research Scientist
John Preskill
Amazon Scholar
Katharine Hyatt
Applied Scientist
James Whitfield
Amazon Visiting Academic
Helmut Katzgraber
Senior Manager, Research Science
Register here
Deep dive with Prime Video leaders
December 13, 12:30 PM - 1:30 PM EST
In this session, the panelists will review how they utilize multimodal machine learning and computer vision to delight millions of Amazon Prime Video customers in 200+ countries. Join as they explore the challenges of solving video understanding at scale, and highlight recent efforts in improving trust, safety, integrity, personalization, quality, and visual effects for video on demand and live content.
Panelists
Ahmed Saad
Senior Manager, Applied Science
Vimal Bhat
Senior Manager, Applied Science
Caren Chen
Manager, Applied Science
Andrew Collins
Senior Manager, Software Development
Register here
NeurIPS 2021: Connecting women in science
December 7, 11:00 PM EST
Join our virtual event and hear from women in tech about their experience at Amazon and why they are passionate about their teams and science. Open only to registered NeurIPS attendees.
Panelists
Cecile Levasseur
Senior Manager, Applied Science
Tara Taghavi
Manager, Applied Science
Rashmi Gangadharaiah
Principal, Applied Scientist
Learn more
Amazon Scholars Michael I. Jordan and Michael Kearns and Amazon distinguished scientist Bernhard Scholkopf NeurIPS Amazon Science.jpg
NeurIPS luminaries on the future of AI
Larry Hardesty
December 02, 2021
Amazon Science hosts a conversation with Amazon Scholars Michael I. Jordan and Michael Kearns and Amazon distinguished scientist Bernhard Schölkopf.
Machine learning

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Applied Scientist, Amazon
US, CA, Sunnyvale
At Amazon Fashion, we are obsessed with making Amazon Fashion the most loved fashion destinations globally. We're searching for Computer Vision pioneers who are passionate about technology, innovation, and customer experience, and who are enthusiastic about making a lasting impact on the industry. You'll be working with talented scientists, engineers, and product managers to innovate on behalf of our customers. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey and change the world of eCommerce forever Key job responsibilities As a Applied Scientist, you will be at the forefront to define, own and drive the science that span multiple machine learning models and enabling multiple product/engineering teams and organizations. You will partner with product management and technical leadership to identify opportunities to innovate customer facing experiences. You will identify new areas of investment and work to align product roadmaps to deliver on these opportunities. As a science leader, you will not only develop unique scientific solutions, but more importantly influence strategy and outcomes across different Amazon organizations such as Search, Personalization and more. This role is inherently cross-functional and requires a strong ability to communicate, influence and earn the trust of software engineers, technical and business leadership. We are open to hiring candidates to work out of one of the following locations: Sunnyvale, CA, USA
Data Scientist, Amazon Fashion
US, CA, Sunnyvale
Are you passionate about solving unique customer-facing problems in the Amazon scale? Are you excited about utilizing statistical analysis, machine learning, data mining and leverage tons of Amazon data to learn and infer customer shopping patterns? Do you enjoy working with a diversity of engineers, machine learning scientists, product managers and user-experience designers? If so, you have found the right match! Fashion is extremely fast-moving, visual, subjective, and it presents numerous unique problem domains such as product recommendations, product discovery and evaluation. The vision for Amazon Fashion is to make Amazon the number one online shopping destination for Fashion customers by providing large selections, inspiring and accurate recommendations and customer experience. The mission of Fit science team as part of Fashion Tech is to innovate and develop scalable ML solutions to provide personalized fit and size recommendation when Amazon Fashion customers evaluate apparels or shoes online. The team is hiring a Data Scientist who has a solid background in Statistical Analysis, Machine Learning and Data Mining and a proven record of effectively analyzing large complex heterogeneous datasets, and is motivated to grow professionally as a Data Scientist. Key job responsibilities - You will work on our Science team and partner closely with applied scientists, data engineers as well as product managers, UX designers, and business partners to answer complex problems via data analysis. Outputs from your analysis will directly help improve the performance of the ML based recommendation systems thereby enhancing the customer experience as well as inform the roadmap for science and the product. - You can effectively analyze complex and disparate datasets collected from diverse sources to derive key insights. - You have excellent communication skills to be able to work with cross-functional team members to understand key questions and earn the trust of senior leaders. - You are able to multi-task between different tasks such as gap analysis of algorithm results, integrating multiple disparate datasets, doing business intelligence, analyzing engagement metrics or presenting to stakeholders. - You thrive in an agile and fast-paced environment on highly visible projects and initiatives. We are open to hiring candidates to work out of one of the following locations: Sunnyvale, CA, USA
Applied Scientist, Amazon Fashion
US, CA, Sunnyvale
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Research Scientist II – Fulfillment Optimization, Network Planning and Fulfillment Execution Science
US, WA, Bellevue
Have you ever wondered how Amazon predicts when your order will arrive and how we ensure that it actually arrives on at the promised date/time? Have you wondered where all those Amazon semi-trucks on the road are headed? Are you passionate about increasing efficiency and reducing carbon footprint? Does the idea of having worldwide impact on Amazon's logistics network including our planes, trucks, and vans sound exciting to you? If so, then we want to talk with you! The Network Planning and Fulfillment Execution team owns and operates OR/ML and simulation systems that continually optimize the distribution of tens of millions of products across Amazon’s warehouses in the most cost-effective manner, utilizing large scale optimization techniques and distributed computing in trying to reduce overall transportation costs while improving the customer experience. 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This will also require you to build machine learning models to predict inventory N weeks out (N<13 Weeks) and ML models to calibrate inventory bounds and math model errors. You will work closely with our product managers and software engineers to disambiguate complex supply chain problems and create ML solutions to solve those problems at scale. You will directly impact our direct customers, and even play with big data and incredible scale in the background. Watch http://bit.ly/amazon-scot to get the big picture. 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A day in the life This is a great role for someone who likes to learn new things. You will have the opportunity to learn all about how Amazon plans for and executes within it's logistics network including Fulfillment Centers, Sort Centers, Delivery Stations, and more. In this role, you will be a design and develop Optimization and Machine Learning models with significant scope, impact, and high visibility. Your solutions will impact business segments worth many-billions-of-dollars and geographies spanning multiple countries and markets. From day one, you will be working with bar raising scientists, engineers, and designers. You will also collaborate with the broader science community in Amazon to broaden the horizon of your work. Successful candidates must thrive in fast-paced environments, which encourage collaborative and creative problem solving, be able to measure and estimate risks, constructively critique peer research, and align research focuses with the Amazon's strategic needs. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their career. About the team Network Planning and Fulfillment Execution Science team contains a group of scientists with different technical backgrounds including Machine Learning and Operations Research, who will collaborate closely with you on your projects. Our team directly supports multiple functional areas across Fulfillment Optimization and the research needs of the corresponding product and engineering teams. We tackle some of the most mathematically complex challenges in facility and transportation planning to improve Amazon's operational efficiency worldwide and at a scale that is unique to Amazon. We often seek the opportunity of applying hybrid techniques in the space of Operations Research and Machine Learning to tackle some of our biggest technical challenges. We disambiguate complex supply chain problems and create ML and optimization solutions to solve those problems at scale. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
Senior Data Scientist, PubTech Data, Insights & Analytics
US, WA, Seattle
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Intern - Economics, PXT Central Science
US, WA, Seattle
The Amazon Economics Team is hiring Economist Interns. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets to solve real-world business problems. Some knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL, UNIX, Sawtooth, and Spark would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, data scientists and MBAʼs. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with future job market placement. Roughly 85% of interns from previous cohorts have converted to full-time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
Applied Scientist, Special Projects
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best. We are a team of doers working passionately to apply cutting-edge advances in technology to solve real-world problems. As an Applied Scientist, you will work with a unique and gifted team developing exciting products for consumers and collaborate with cross-functional teams. Our team rewards intellectual curiosity while maintaining a laser-focus in bringing entirely new products to Amazon. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the cutting edge of both academic and applied research in this product area, you have the opportunity to work together with some of the most talented scientists, engineers, and product managers. Key job responsibilities We are looking for a computational focused candidate with experience predicting, folding, and designing proteins. The candidate can expect to work on a small team working collaboratively with software developers, engineers, and like-minded scientists on a fast paced project. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
Senior Applied Scientist, Sponsored Products
US, NY, New York
Amazon is investing heavily in building a world-class advertising business, and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. We deliver billions of ad impressions and millions of clicks daily and break fresh ground to create world-class products. We are highly motivated, collaborative, and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. Our systems and algorithms operate on one of the world's largest product catalogs, matching shoppers with advertised products with a high relevance bar and strict latency constraints. Sponsored Products Detail Page Blended Widgets team is chartered with building novel product recommendation experiences. We push the innovation frontiers for our hundreds of millions of customers WW to aid product discovery while helping shoppers to find relevant products easily. Our team is building differentiated recommendations that highlight specific characteristics of products (either direct attributes, inferred or machine learned), and leveraging generative AI to provide interactive shopping experiences. We are looking for a Senior Applied Scientist who can delight our customers by continually learning and inventing. Our ideal candidate is an experienced Applied Scientist who has a track-record of performing deep analysis and is passionate about applying advanced ML and statistical techniques to solve real-world, ambiguous and complex challenges to optimize and improve the product performance, and who is motivated to achieve results in a fast-paced environment. The position offers an exceptional opportunity to grow your technical and non-technical skills and make a real difference to the Amazon Advertising business. As a Senior Applied Scientist on this team, you will: * Be the technical leader in Machine Learning; lead efforts within this team and collaborate across teams * Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, perform hands-on analysis and modeling of enormous data sets to develop insights that improve shopper experiences and merchandise sales * Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. * Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. * Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. * Research new and innovative machine learning approaches. * Promote the culture of experimentation and applied science at Amazon Team video https://youtu.be/zD_6Lzw8raE We are also open to consider the candidate in Seattle, or Palo Alto. We are open to hiring candidates to work out of one of the following locations: New York, NY, USA
Senior Applied Scientist, Amazon
US, VA, Arlington
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Search Sourcing and Relevance team parses billions of ads to surface the best ad to show to Amazon shoppers. The team strives to understand customer intent and identify relevant ads that enable them to discover new and alternate products. This also enables sellers on Amazon to showcase their products to customers, which may, at times, be buried deeper in the search results. By showing the right ads to customers at the right time, this team improves the shopper experience, increase advertiser ROI, and improves long-term monetization. This is a talented team of machine learning scientists and software engineers working on complex solutions to understand the customer intent and present them with ads that are not only relevant to their actual shopping experience but also non-obtrusive. This area is of strategic importance to Amazon Retail and Marketplace business, driving long term growth. Key job responsibilities As a Senior Applied Scientist on this team, you will: - Be the technical leader in Machine Learning; lead efforts within this team and across other teams. - Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Run A/B experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Research new and innovative machine learning approaches. - Recruit Applied Scientists to the team and provide mentorship. About the team Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA
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