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A quick guide to Amazon’s papers at NeurIPS 2023

Amid topics ranging from experimental design and human-robot interaction to recommender systems and vision-language models, reinforcement learning emerges as a particular focus.

By Staff writer
December 12, 2023
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The Conference on Neural Information Processing Systems (NeurIPS) takes place this week, and the Amazon papers accepted there touch on a wide range of topics, from experimental design and human-robot interaction to recommender systems and real-time statistical estimation. Amid that diversity, a few topics come in for particular attention: optimization, privacy, tabular data, time series forecasting, vision-language models — and particularly reinforcement learning.

Code generation

Large language models of code fail at completing code with potential bugs
Tuan Dinh, Jinman Zhao, Samson Tan, Renato Negrinho, Leonard Lausen, Sheng Zha, George Karypis

Complex query answering

Complex query answering on eventuality knowledge graph with implicit logical constraints
Jiaxin Bai, Xin Liu, Weiqi Wang, Chen Luo, Yangqiu Song

Complex query answering.png
An example of a complex eventuality query, with its computational and informational atomics. V is something that happens before a person complains and leaves a restaurant; according to the knowledge graph, V could be either “Service is bad” or “Food is bad”. If V? is the reason for V, then according to the graph, V? could be either “Staff is new”, “PersonY adds ketchup”, “PersonY adds soy sauce”, or “PersonY adds vinegar”. However, from the query, we know that “PersonY adds vinegar” does not happen, and “PersonY adds soy sauce” happens after “food is bad”, so it can’t be the reason for “food is bad”. From “Complex query answering on eventuality knowledge graph with implicit logical constraints”.

Experimental design

Experimental designs for heteroskedastic variance
Justin Weltz, Tanner Fiez, Eric Laber, Alexander Volfovsky, Blake Mason, Houssam Nassif, Lalit Jain

Federated learning

Federated multi-objective learning
Haibo Yang, Zhuqing Liu, Jia Liu, Chaosheng Dong, Michinari Momma

Human-robot interaction

Alexa Arena: A user-centric interactive platform for embodied AI
Qiaozi (QZ) Gao, Govind Thattai, Suhaila Shakiah, Xiaofeng Gao, Shreyas Pansare, Vasu Sharma, Gaurav Sukhatme, Hangjie Shi, Bofei Yang, Desheng Zhang, Lucy Hu, Karthika Arumugam, Shui Hu, Matthew Wen, Dinakar Guthy, Cadence Chung, Rohan Khanna, Osman Ipek, Leslie Ball, Kate Bland, Heather Rocker, Michael Johnston, Reza Ghanadan, Dilek Hakkani-Tür, Prem Natarajan

Optimization

Bounce: Reliable high-dimensional Bayesian optimization for combinatorial and mixed spaces
Leonard Papenmeier, Luigi Nardi, Matthias Poloczek

Debiasing conditional stochastic optimization
Lie He, Shiva Kasiviswanathan

Distributionally robust Bayesian optimization with ϕ-divergences
Hisham Husain, Vu Nguyen, Anton van den Hengel

Ordinal classification

Conformal prediction sets for ordinal classification
Prasenjit Dey, Srujana Merugu, Sivaramakrishnan (Siva) Kaveri

Privacy

Creating a public repository for joining private data
James Cook, Milind Shyani, Nina Mishra

Joining private data.png
A stylized illustration of the repository problem. The sender S uploads a private count sketch capturing which people do and do not have cancer. The receiver R uses the sketch to decorate her data (people's work locations) with a noisy version of S’s cancer column. Two noisy columns are generated: one for cancer (+1) and one for not (−1). R can then build a machine learning model to predict whether employees who work near a toxic waste site are more likely to develop cancer. From “Creating a public repository for joining private data”.

Scalable membership inference attacks via quantile regression
Martin Bertran Lopez, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu

Real-time statistical estimation

Online robust non-stationary estimation
Abishek Sankararaman, Balakrishnan (Murali) Narayanaswamy

Recommender systems

Enhancing user intent capture in session-based recommendation with attribute patterns
Xin Liu, Zheng Li, Yifan Gao, Jingfeng Yang, Tianyu Cao, Zhengyang Wang, Bing Yin, Yangqiu Song

Reinforcement learning

Budgeting counterfactual for offline RL
Yao Liu, Pratik Chaudhari, Rasool Fakoor

Finite-time logarithmic Bayes regret upper bounds
Alexia Atsidakou, Branislav Kveton, Sumeet Katariya, Constantine Caramanis, Sujay Sanghavi

Resetting the optimizer in deep RL: An empirical study
Kavosh Asadi, Rasool Fakoor, Shoham Sabach

TD convergence: An optimization perspective
Kavosh Asadi, Shoham Sabach, Yao Liu, Omer Gottesman, Rasool Fakoor

Responsible AI

Improving fairness for spoken language understanding in atypical speech with text-to-speech
Helin Wang, Venkatesh Ravichandran, Milind Rao, Becky Lammers, Myra Sydnor, Nicholas Maragakis, Ankur A. Butala, Jayne Zhang, Victoria Chovaz, Laureano Moro-Velazquez

Tabular data

An inductive bias for tabular deep learning
Ege Beyazit, Jonathan Kozaczuk, Bo Li, Vanessa Wallace, Bilal Fadlallah

HYTREL: Hypergraph-enhanced tabular data representation learning
Pei Chen, Soumajyoti Sarkar, Leonard Lausen, Balasubramaniam Srinivasan, Sheng Zha, Ruihong Huang, George Karypis

Table as hypergraph.png
An example of modeling a table as a hypergraph. Cells make up the nodes, and the cells in each row, each column, and the entire table form hyperedges. The table caption and the header names provide the names of the table and column hyperedges. The hypergraph keeps the four structural properties of tables — i.e., row/column permutations result in the same hypergraph. From “HYTREL: Hypergraph-enhanced tabular data representation learning”.

Time series forecasting

Predict, refine, synthesize: Self-guiding diffusion models for probabilistic time series forecasting
Marcel Kollovieh, Abdul Fatir Ansari, Michael Bohlke-Schneider, Jasper Zschiegner, Hao Wang, Yuyang (Bernie) Wang

PreDiff: Precipitation nowcasting with latent diffusion models
Zhihan Gao, Xingjian Shi, Boran Han, Hao Wang, Xiaoyong Jin, Danielle Maddix Robinson, Yi Zhu, Mu Li, Yuyang (Bernie) Wang

Vision-language models

Prompt pre-training with twenty-thousand classes for open-vocabulary visual recognition
Shuhuai Ren, Aston Zhang, Yi Zhu, Shuai Zhang, Shuai Zheng, Mu Li, Alex Smola, Xu Su

Your representations are in the network: Composable and parallel adaptation for large scale models
Yonatan Dukler, Alessandro Achille, Hao Yang, Ben Bowman, Varsha Vivek, Luca Zancato, Avinash Ravichandran, Charless Fowlkes, Ashwin Swaminathan, Stefano Soatto

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Principal Applied Scientist, Automated Reasoning in Identity
US, CA, Santa Clara
Are you passionate about applying automated reasoning and program analysis to real world problems? Do you want to create products that help customers? If so, then we have an exciting opportunity for you. We’re looking for an Applied Scientist to help strengthen our customers' security with automation for managed controls. AWS Identity provides the bedrock for secure and continuous access to all AWS services. By quickly connecting millions of users, across the world we empower organizations and enterprises to accelerate their cloud and digital transformation. In this role, you will interact with internal teams and external customers to understand their requirements. You will apply your knowledge to propose innovative solutions, create software prototypes, and productize prototypes into production systems using software development tools and methodologies. In addition, you will support and scale your solutions to meet the ever growing demand of customer use. Key job responsibilities * Interact with various teams to develop an understanding of their security and safety requirements. * Apply the acquired knowledge to build tools and algorithms, find problems, or show the absence of security/safety problems. * Implement these capabilities through the use of Automated Reasoning and various concepts from programming languages. * Perform analysis of the customer systems using tools developed in-house or externally provided * Create software prototypes to verify and validate the devised solutions methodologies; integrate the prototypes into production systems using standard software development tools and methodologies. About the team About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. This team is part of AWS Utility Computing: Utility Computing (UC) AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services.
Economist, Prime Science
US, WA, Seattle
Amazon Prime is looking for an ambitious Economist to help create econometric insights for world-wide Prime. Prime is Amazon's premiere membership program, with over 200M members world-wide. This role is at the center of many major company decisions that impact Amazon's customers. These decisions span a variety of industries, each reflecting the diversity of Prime benefits. These range from fast-free e-commerce shipping, digital content (e.g., exclusive streaming video, music, gaming, photos), and grocery offerings. Prime Science creates insights that power these decisions. As an economist in this role, you will create statistical tools that embed causal interpretations. You will utilize massive data, state-of-the-art scientific computing, econometrics (causal, counterfactual/structural, time-series forecasting, experimentation), and machine-learning, to do so. Some of the science you create will be publishable in internal or external scientific journals and conferences. You will work closely with a team of economists, applied scientists, data professionals (business analysts, business intelligence engineers), product managers, and software engineers. You will create insights from descriptive statistics, as well as from novel statistical and econometric models. You will create internal-to-Amazon-facing automated scientific data products to power company decisions. You will write strategic documents explaining how senior company leaders should utilize these insights to create sustainable value for customers. These leaders will often include the senior-most leaders at Amazon. The team is unique in its exposure to company-wide strategies as well as senior leadership. It operates at the cutting-edge of utilizing data, econometrics, artificial intelligence, and machine-learning to form business strategies. A successful candidate will have demonstrated a capacity for building, estimating, and defending statistical models (e.g., causal, counterfactual, time-series, machine-learning) using software such as R, Python, or STATA. They will have a willingness to learn and apply a broad set of statistical and computational techniques to supplement deep-training in one area of econometrics. For example, many applications on the team use structural econometrics, machine-learning, and time-series forecasting. They rely on building scalable production software, which involves a broad set of world-class software-building skills often learned on-the-job. As a consequence, already-obtained knowledge of SQL, machine learning, and large-scale scientific computing using distributed computing infrastructures such as Spark-Scala or PySpark would be a plus. Additionally, this candidate will show a track-record of delivering projects well and on-time, preferably in collaboration with other team members (e.g. co-authors). Candidates must have very strong writing and emotional intelligence skills (for collaborative teamwork, often with colleagues in different functional roles), a growth mindset, and a capacity for dealing with a high-level of ambiguity. Endowed with these traits and on-the-job-growth, the role will provide the opportunity to have a large strategic, world-wide impact on the customer experiences of Prime members.
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