Downtown New Orleans, Louisiana and the Missisippi River
NeurIPS 2022
November 28 - December 9, 2022
New Orleans, Louisiana

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. To learn more about Amazon's papers, read our quick guide.

Amazon team

Our organizing committee members.

Accepted publications

Workshops

NeurIPS 2022 Workshop on LatinX in AI Research
November 28
The workshop is a one-day event with invited speakers, oral presentations, and posters. The event brings together faculty, graduate students, research scientists, and engineers for an opportunity to connect and exchange ideas. There will be a panel discussion and a mentoring session to discuss current research trends and career choices in artificial intelligence and machine learning.

Website: https://www.latinxinai.org/neurips-2022
NeurIPS 2022 Workshop on Women in Machine Learning
November 28
The 17th Workshop for Women in Machine Learning (WiML) brings together members of the academic and industry research landscape for an opportunity to connect and exchange ideas, and learn from each other.

Amazon organizer: Sergül Aydöre

Website: https://sites.google.com/view/wiml2022
NeurIPS 2022 Workshop on Graph ML
November 28, 2:00 PM EST
At AWS, we aim at lowering the bar in productizing graph machine learning (GML). Neptune ML facilitates this goal and helps customers obtain real time GNN predictions with graph databases using graph query languages. Amazon develops frameworks based on DGL to solve internal and external GML problems and realize the impact of GNNs.

Amazon organizers: Vassilis Ioannidis, Zak Jost, Minjie Wang, David Paul Wipf

Website: https://sites.google.com/view/dgl-workshop-neurips-2022
NeurIPS 2022 Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems
December 2
This workshop brings together researchers from different communities to share ideas and success stories. By showcasing key applied challenges, along with recent theoretical advances, we hope to foster connections and prompt fruitful discussion. We invite researchers to submit extended abstracts for contributed talks and posters.
NeurIPS 2022 Workshop on Federated Learning: Recent Advances and New Challenges
December 2
The goal of this workshop is to bring together researchers and practitioners interested in FL. This day-long event will facilitate interaction among students, scholars, and industry professionals from around the world to understand the topic, identify technical challenges, and discuss potential solutions.

Amazon organizer: Olivia Choudhury

Amazon program committee member: Berivan Isik

Website: https://federated-learning.org/fl-neurips-2022
NeurIPS 2022 Workshop on SyntheticData4ML
December 2
This workshop brings together research communities in generative models, privacy, and fairness as well as industry leaders in a joint effort to develop the theory, methodology, and algorithms to generate synthetic benchmark datasets with the goal of enabling ethical and reproducible ML research. The call for papers submission deadline is September 22, 11:59pm.

Amazon organizer: Sergül Aydöre

Amazon panelist: Shuai Tang

Website: https://www.syntheticdata4ml.vanderschaar-lab.com
NeurIPS 2022 Workshop on Efficient Natural Language and Speech Processing (ENLSP)
December 2
The second version of the Efficient Natural Language and Speech Processing (ENLSP) workshop focuses on fundamental and challenging problems to make natural language and speech processing (especially pre-trained models) more efficient in terms of data, model, training, and inference.

Amazon panelist: Rahul Gupta

Amazon technical committee: Can Liu, Amina Shabbeer, M. Skylar Versage, Tanya Roosta

Website: https://neurips2022-enlsp.github.io
NeurIPS 2022 Workshop on Causality for Real-world Impact
December 2
Causality has a long history, providing it with many principled approaches to identify a causal effect (or even distill cause from effect). However, these approaches are often restricted to very specific situations, requiring very specific assumptions. This contrasts heavily with recent advances in machine learning. Real-world problems aren’t granted the luxury of making strict assumptions, yet still require causal thinking to solve. Armed with the rigor of causality, and the can-do-attitude of machine learning, we believe the time is ripe to start working towards solving real-world problems.

Amazon speaker and panelist: Bernhard Schölkopf

Website: https://www.cml-4-impact.vanderschaar-lab.com
NeurIPS 2022 Workshop on New Frontiers in Graph Learning
December 2
The primary goal of this workshop is to expand the impact of graph learning beyond the current boundaries. We believe that graph, or relation data, is a universal language that can be used to describe the complex world. Ultimately, we hope graph learning will become a generic tool for learning and understanding any type of (structured) data.
NeurIPS 2022 Workshop on Offline RL as a Launchpad
December 2
While offline RL focuses on learning solely from fixed datasets, one of the main learning points from the previous edition of offline RL workshop was that large-scale RL applications typically want to use offline RL as part of a bigger system as opposed to being the end-goal in itself. Thus, we propose to shift the focus from algorithm design and offline RL applications to how offline RL can be a launchpad , i.e., a tool or a starting point, for solving challenges in sequential decision-making such as exploration, generalization, transfer, safety, and adaptation.
NeurIPS 2022 Workshop on Interpolation and Beyond
December 2
This workshop brings together researchers and users of interpolation regularizers to foster research and discussion to advance and understand interpolation regularizers. This inaugural meeting will have no shortage of interactions and energy to achieve these exciting goals. We are reserving a few complimentary workshop registrations for accepted paper authors who would otherwise have difficulty attending. Please reach out if this applies to you.

Amazon program committee members: Tong He, Mohammad Kachuee, Harshavardhan Sundar

Website: https://sites.google.com/view/interpolation-workshop
NeurIPS 2022 Workshop on Score-Based Methods
December 2
A workshop to bring together researchers who use score-based methods in machine learning and statistics.
NeurIPS 2022 Workshop on Human in the Loop Learning
December 2
The HiLL workshop aims to bring together researchers and practitioners working on the broad areas of HiLL, ranging from interactive/active learning algorithms for real-world decision-making systems (e.g., autonomous driving vehicles, robotic systems, etc.), human-inspired learning that mitigates the gap between human intelligence and machine intelligence, human-machine collaborative learning that creates a more powerful learning system, lifelong learning that transfers knowledge to learn new tasks over a lifetime, as well as interactive system designs (e.g., data visualization, annotation systems, etc.).

Website: https://neurips-hill.github.io
NeurIPS 2022 Workshop on Table Representation Learning
December 2, 9:30 AM - 6:45 PM EST
The Table Representation Learning workshop is the first workshop in this emerging research area and has the following main goals: 1) motivating tabular data as a first-class modality for representation learning and further shaping this area, 2) show-casing impactful applications of pretrained table models and discussing future opportunities thereof, and 3) facilitating discussion and collaboration across the machine learning, natural language processing, and data management communities.

Website: https://table-representation-learning.github.io/
NeurIPS 2022 Workshop on Reinforcement Learning for Real Life Workshop (RL4RealLife)
December 3
The main goals of the workshop are to: identify key research problems that are critical for the success of real-world applications; report progress on addressing these critical issues; and have practitioners share their success stories of applying RL to real-world problems, and the insights gained from such applications.

Amazon co-chair: Lihong Li, Yao Liu

Website: https://sites.google.com/view/RL4RealLife
NeurIPS 2022 Workshop Decentralization and Trustworthy Machine Learning in Web3
December 3
This workshop focuses on how future researchers and practitioners should prepare themselves to achieve different trustworthiness requirements, such as security and privacy in machine learning through decentralization and blockchain techniques, as well as how to leverage machine learning techniques to automate some processes in current decentralized systems and ownership economies in Web3.

Amazon organizer: Bo Li

Website: https://ai-secure.github.io/DMLW2022/
NeurIPS 2022 Workshop on a Causal View on Dynamical Systems
December 3
In this workshop, we bring together researchers in dynamical systems, time-series methods, causality, infinite-depth neural networks, and machine learning. We believe a side-by-side discussion of dynamical systems and causal inference (discovery and estimation) will allow one to develop novel approaches, transfer expertise across communities, and enable us to overcome current limitations of each individual perspective. Connections to other scientific disciplines as well as practitioners’ view will be highlighted to showcase successful applications of causal inference in dynamical settings.

Amazon organizer: Yuyang (Bernie) Wang

Website: https://sites.google.com/view/caudyn2022
NeurIPS 2022 Workshop on Transfer Learning for NLP
December 3
Transfer learning has become ubiquitous in natural language processing due in part to the ease of access to large pre-trained language models (PLM). Current transfer learning methods, combined with PLMs, have seen outstanding successes in transferring knowledge to new tasks, domains, and even languages. However, existing methods still suffer from some common weaknesses that restrict their potential applications.

One particular hope for this workshop is to help to answer the question: Can we characterize the transfer behaviors between source and target tasks/domains/languages in terms of their fundamental properties?

Amazon program committee member: Alham Fikri Aji

Website: https://tl4nlp.github.io
NeurIPS 2022 Workshop on Distribution Shifts (DistShifts)
December 3
This workshop brings together domain experts and ML researchers working on mitigating distribution shifts in real-world applications.

Website: https://sites.google.com/view/distshift2022
NeurIPS 2022 Workshop on Self-Supervised Learning - Theory and Practice
December 3
In the 3rd iteration of this workshop, we continue to bridge this gap between theory and practice. We bring together SSL-interested researchers from various domains to discuss the theoretical foundations of empirically well-performing SSL approaches and how the theoretical insights can further improve SSL’s empirical performance.
NeurIPS 2022 Workshop on Trojan Detection Challenge
December 8
In this competition, we challenge you to detect and analyze Trojan attacks on deep neural networks that are designed to be difficult to detect. Neural Trojans are a growing concern for the security of ML systems, but little is known about the fundamental offense-defense balance of Trojan detection. Early work suggests that standard Trojan attacks may be easy to detect [1], but recently it has been shown that in simple cases one can design practically undetectable Trojans.

Amazon organizer: Bo Li

Website: https://trojandetection.ai
NeurIPS 2022 Workshop on the Symbiosis of Deep Learning and Differential Equations (DLDE)
December 14
This workshop will aim to bring together researchers with backgrounds in computational science and deep learning to encourage intellectual exchanges, cultivate relationships and accelerate research in this area. The scope of the workshop spans topics at the intersection of DL and DEs, including theory of DL and DEs, neural differential equations, solving DEs with neural networks, and more.

Amazon organizer: Archis Joglekar

Website: https://dlde-2022.github.io
NeurIPS 2022 Has It Trained Yet Workshop
December 2
NeurIPS 2022 Workshop on Trustworthy and Socially Responsible Machine Learning (TSRML)
December 9
This workshop aims to bring together researchers interested in the emerging and interdisciplinary field of trustworthy and socially responsible machine learning from a broad range of disciplines with different perspectives to this problem. We attempt to highlight recent related work from different communities, clarify the foundations of trustworthy machine learning, and chart out important directions for future work and cross-community collaborations.
NeurIPS 2022 Workshop on Trustworthy Embodied AI
December 2
NeurIPS 2022 Workshop on Machine Learning for Structural Biology
December 3
In this exciting time for the field, our workshop, “Machine Learning in Structural Biology” (MLSB), seeks to bring together relevant experts, practitioners, and students across a broad community to focus on these challenges and opportunities. We believe the union of these communities, including the geometric and graph learning communities, NLP researchers, and structural biologists with domain expertise at our workshop can help spur new ideas, spark collaborations, and advance the impact of machine learning in structural biology. Progress at this intersection promises to unlock new scientific discoveries and the ability to design novel medicines.
NeurIPS 2022 Workshop on All Things Attention: Bridging Different Perspectives on Attention
December 2
The All Things Attention workshop aims to foster connections across disparate academic communities that conceptualize “attention” such as Neuroscience, Psychology, Machine Learning, and Human Computer Interaction.



Related content

Learn more about Amazon's research at NeurIPS.
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US, WA, Seattle
This is a unique opportunity to build technology and science that millions of people will use every day. Are you excited about working on large scale Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL)? We are embarking on a multi-year journey to improve the shopping experience for customers globally. Amazon Search team creates customer-focused search solutions and technologies that makes shopping delightful and effortless for our customers. Our goal is to understand what customers are looking for in whatever language happens to be their choice at the moment and help them find what they need in Amazon's vast catalog of billions of products. As Amazon expands to new geographies, we are faced with the unique challenge of maintaining the bar on Search Quality due to the diversity in user preferences, multilingual search and data scarcity in new locales. We are looking for an applied researcher to work on improving search on Amazon using NLP, ML, and DL technology. As an Applied Scientist, you will lead our efforts in query understanding, semantic matching (e.g. is a drone the same as quadcopter?), relevance ranking (what is a "funny halloween costume"?), language identification (did the customer just switch to their mother tongue?), machine translation (猫の餌を注文する). This is a highly visible role with a huge impact on Amazon customers and business. As part of this role, you will develop high precision, high recall, and low latency solutions for search. Your solutions should work for all languages that Amazon supports and will be used in all Amazon locales world-wide. You will develop scalable science and engineering solutions that work successfully in production. You will work with leaders to develop a strategic vision and long term plans to improve search globally. We are growing our collaborative group of engineers and applied scientists by expanding into new areas. This is a position on Global Search Quality team in Seattle Washington. We are moving fast to change the way Amazon search works. Together with a multi-disciplinary team you will work on building solutions with NLP/ML/DL at its core. Along the way, you’ll learn a ton, have fun and make a positive impact on millions of people. Come and join us as we invent new ways to delight Amazon customers.
US, WA, Seattle
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US, CA, San Francisco
The retail pricing science and research group is a team of scientists and economists who design and implement the analytics powering pricing for Amazon's on-line retail business. The team uses world-class analytics to make sure that the prices for all of Amazon's goods and services are aligned with Amazon's corporate goals. We are seeking an experienced high-energy Economist to help envision, design and build the next generation of retail pricing capabilities. You will work at the intersection of statistical inference, experimentation design, economic theory and machine learning to design new methods and pricing strategies for assessing pricing innovations. Roughly 85% of previous intern cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. Key job responsibilities Amazon's Pricing Science and Research team is seeking an Economist to help envision, design and build the next generation of pricing capabilities behind Amazon's on-line retail business. As an economist on our team, you will will have the opportunity to work with our unprecedented retail data to bring cutting edge research into real world applications, and communicate the insights we produce to our leadership. This position is perfect for someone who has a deep and broad analytic background and is passionate about using mathematical modeling and statistical analysis to make a real difference. You should be familiar with modern tools for data science and business analysis. We are particularly interested in candidates with research background in experimentation design, applied microeconomics, econometrics, statistical inference and/or finance. A day in the life Discussions with business partners, as well as product managers and tech leaders to understand the business problem. Brainstorming with other scientists and economists to design the right model for the problem in hand. Present the results and new ideas for existing or forward looking problems to leadership. Deep dive into the data. Modeling and creating working prototypes. Analyze the results and review with partners. Partnering with other scientists for research problems. About the team The retail pricing science and research group is a team of scientists and economists who design and implement the analytics powering pricing for Amazon's on-line retail business. The team uses world-class analytics to make sure that the prices for all of Amazon's goods and services are aligned with Amazon's corporate goals.
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We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX 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, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with 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.
US
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US, WA, Seattle
The Selling Partner Fees team owns the end-to-end fees experience for two million active third party sellers. We own the fee strategy, fee seller experience, fee accuracy and integrity, fee science and analytics, and we provide scalable technology to monetize all services available to third-party sellers. We are looking for an Intern Economist with excellent coding skills to design and develop rigorous models to assess the causal impact of fees on third party sellers’ behavior and business performance. As a Science Intern, you will have access to large datasets with billions of transactions and will translate ambiguous fee related business problems into rigorous scientific models. You will work on real world problems which will help to inform strategic direction and have the opportunity to make an impact for both Amazon and our Selling Partners.