Get to know the Amazon research community at NeurIPS 2019!
Amazon’s research teams are looking forward to meeting you at NeurIPS 2019!
Come and visit us at the Amazon booth, and read on for more information about academic collaboration, career opportunities, and our teams.
Here is how Amazon is participating at NeurIPS:
Organizing Committee Members
- Bernhard Schölkopf - Advisory Board
- Michael I. Jordan - Advisory Board
- Thorsten Joachims - Senior Area Chair
- Anshumali Shrivastava - Area Chair
- Cedric Archambeau – Area Chair
- Peter Gehler - Area Chair
- Sujay Sanghavi – Committee Member
Publications at NeurIPS
Tuesday, 12/10 | 10:45-12:45pm | East Exhibition Hall B&C
- A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning | #192 | Francisco Garcia (UMass Amherst/Amazon) · Philip Thomas (UMass Amherst)
- Blocking Bandits | #17 | Soumya Basu (UT Austin) · Rajat Sen (UT Austin & Amazon) · Sujay Sanghavi (UT Austin/Amazon) · Sanjay Shakkottai (UT Austin)
- Causal Regularization | #180 | Dominik Janzing (Amazon)
- Communication-efficient Distributed SGD with Sketching | #81 | Nikita Ivkin (Amazon) · Daniel Rothchild (University of California, Berkeley) · Md Enayat Ullah (Johns Hopkins University) · Vladimir Braverman (Johns Hopkins University) · Ion Stoica (UC Berkeley) · Raman Arora (Johns Hopkins University)
- Learning Distributions Generated by One-Layer ReLU Networks | #49 | Shanshan Wu (UT Austin), Alexandros G. Dimakis (UT Austin), Sujay Sanghavi (UT Austin/Amazon)
Tuesday, 12/10 | 5:30-7:30pm | East Exhibition Hall B&C
- Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control | #195 | Sai Qian Zhang (Harvard University) · Qi Zhang (Amazon) · Jieyu Lin (University of Toronto)
- Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products | #37 | Tharun Kumar Reddy Medini (Rice University) · Qixuan Huang (Rice University) · Yiqiu Wang (Massachusetts Institute of Technology) · Vijai Mohan (Amazon) · Anshumali Shrivastava (Amazon / Rice University)
- Iterative Least Trimmed Squares for Mixed Linear Regression | #50 | Yanyao Shen (UT Austin), Sujay Sanghavi (UT Austin & Amazon)
- Meta-Surrogate Benchmarking for Hyperparameter Optimization | #6 | Aaron Klein (Amazon) · Zhenwen Dai (Spotify) · Frank Hutter (University of Freiburg) · Neil Lawrence (University of Cambridge) · Javier Gonzalez (Amazon)
- Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations | #32 | Debraj Basu (Adobe) · Deepesh Data (UCLA) · Can Karakus (Amazon) · Suhas Diggavi (UCLA)
- Selecting causal brain features with a single conditional independence test per feature | #139 | Atalanti Mastakouri (Max Planck Institute for Intelligent Systems) · Bernhard Schölkopf (Amazon/MPI for Intelligent Systems) · Dominik Janzing (Amazon)
Wednesday, 12/11 | 10:45-12:45pm | East Exhibition Hall B&C
- On Single Source Robustness in Deep Fusion Models | #93 | Taewan Kim (Amazon) · Joydeep Ghosh (UT Austin)
- Perceiving the arrow of time in autoregressive motion | #155 | Kristof Meding (University Tübingen) · Dominik Janzing (Amazon) · Bernhard Schölkopf (Amazon / MPI for Intelligent Systems) · Felix A. Wichmann (University of Tübingen)
Wednesday, 12/11 | 5:00-7:00pm | East Exhibition Hall B&C
- Compositional De-Attention Networks | #127 | Yi Tay (Nanyang Technological University) · Anh Tuan Luu (MIT) · Aston Zhang (Amazon) · Shuohang Wang (Singapore Management University) · Siu Cheung Hui (Nanyang Technological University)
- Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing | #3 | Jonas Mueller (Amazon) · Vasilis Syrgkanis (Microsoft Research) · Matt Taddy (Amazon)
- MaxGap Bandit: Adaptive Algorithms for Approximate Ranking | #4 | Sumeet Katariya (Amazon/University of Wisconsin-Madison) · Ardhendu Tripathy (UW Madison) · Robert Nowak (UW Madison)
- Primal-Dual Block Generalized Frank-Wolfe | #165 | Qi Lei (UT Austin) · Jiacheng Zhuo (UT Austin) · Constantine Caramanis (UT Austin) · Inderjit S Dhillon (Amazon/UT Austin) · Alexandros Dimakis (UT Austin)
- Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling | #208 | Tengyang Xie (University of Illinois at Urbana-Champaign) · Yifei Ma (Amazon) · Yu-Xiang Wang (UC Santa Barbara)
Thursday, 12/12 | 10:45-12:45pm | East Exhibition Hall B&C
- AutoAssist: A Framework to Accelerate Training of Deep Neural Networks | #155 | Jiong Zhang (UT Austin) · Hsiang-Fu Yu (Amazon) · Inderjit S Dhillon (Amazon / UT Austin)
- Exponentially convergent stochastic k-PCA without variance reduction | #200 (oral 12/12 10:05-10:20 W ballroom c) | Cheng Tang (Amazon)
- Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift | #54 | Stephan Rabanser (Amazon / Technical University of Munich) · Stephan Günnemann (Technical University of Munich) · Zachary Lipton (Amazon / Carnegie Mellon University)
- High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes | #107 | David Salinas (Naverlabs) · Michael Bohlke-Schneider (Amazon) · Laurent Callot (Amazon) · Jan Gasthaus (Amazon) · Roberto Medico (Ghent University)
- Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning | #30 | Valerio Perrone (Amazon) · Huibin Shen (Amazon) · Matthias Seeger (Amazon) · Cedric Archambeau (Amazon) · Rodolphe Jenatton (Amazon)
- Mo’States Mo’Problems: Emergency Stop Mechanisms from Observation | #227 | Samuel Ainsworth (University of Washington) · Matt Barnes (University of Washington) · Siddhartha Srinivasa (Amazon/University of Washington)
- Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting | #113 | Rajat Sen (Amazon) · Hsiang-Fu Yu (Amazon) · Inderjit S Dhillon (Amazon/UT Austin)
Thursday, 12/12 | 5:00-7:00pm | East Exhibition Hall B&C
- Dynamic Local Regret for Non-convex Online Forecasting | #20 | Sergul Aydore (Stevens Institute of Technology) · Tianhao Zhu (Stevens Institute of Technology) · Dean Foster (Amazon)
- Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space | #47 | Suo Yang (UT Austin) Yanyao Shen (UT Austin), Sujay Sanghavi (UT Austin & Amazon)
- Inverting Deep Generative models, One layer at a time | #48 |Qi Lei (University of Texas at Austin) · Ajil Jalal (UT Austin) · Inderjit S Dhillon (Amazon/ UT Austin) · Alexandros Dimakis (UT Austin)
- Provable Non-linear Inductive Matrix Completion| #215 | Kai Zhong (Amazon) · Zhao Song (UT Austin) · Prateek Jain (Microsoft Research) · Inderjit S Dhillon (Amazon / UT Austin)
- Learning with Rich Experience: Integration of Learning Paradigms
- Meta-Q-Learning | Rasool Fakoor, Pratik Chaudhari, Stefano Soatto, Alexander J. Smola
- Human-Centric Machine Learning
- Learning Fair and Transferable Representations | Luco Oneto, Michele Donini, Andreas Maurer, Massimiliano Pontil
- Bayesian Deep Learning
- "Online Bayesian Learning for E-Commerce Query Reformulation" | Gaurush Hiranandani, Sumeet Katariya, Nikhil Rao, Karthik Subbian
- "Constrained Bayesian Optimization with Max-Value Entropy Search" | Valerio Perrone, Iaroslav Shcherbatyi, Rodolphe Jenatton, Cedric Archambeau, Matthias Seeger
- "A quantile-based approach to hyperparameter transfer learning" | David Salinas, Huibin Shen, Valerio Perrone
- "A Baseline for Few-Shot Image Classification" | Guneet Singh Dhillon, Pratik Chaudhari, Avinash Ravichandran, Stefano Soatto
- Conversational AI
- Organizer: Dilek Hakkani-Tür
- The Eighth Dialog System Technology Challenge | Seokhwan Kim, Michel Galley, Chulaka Gunasekara, Sungjin Lee, Adam Atkinson, Baolin Peng, Hannes Schulz, Jianfeng Gao, Jinchao Li, Mahmoud Adada, Minlie Huang, Luis Lastras, Jonathan K. Kummerfeld, Walter S. Lasecki, Chiori Hori, Anoop Cherian, Tim K. Marks, Abhinav Rastogi, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta
- “Just Ask: An Interactive Learning Framework for Vision and Language Navigation” | Ta-Chung Chi, Minmin Shen, Mihail Eric, Seokhwan Kim, Dilek Hakkani-Tur
- “MA-DST: Multi-Attention-Based Scalable Dialog State Tracking” | Adarsh Kumar, Peter Ku, Anuj Kumar Goyal, Angeliki Metallinou, Dilek Hakkani-Tur
- “Investigation of Error Simulation Techniques for Learning Dialog Policies for Conversational Error Recovery” | Maryam Fazel-Zarandi, Longshaokan Wang, Aditya Tiwari, Spyros Matsoukas
- “Towards Personalized Dialog Policies for Conversational Skill Discovery”| Maryam Fazel-Zarandi, Sampat Biswas, Ryan Summers, Ahmed Elmalt, Andy McCraw, Michael McPhillips, John Peach
- “Conversation Quality Evaluation via User Satisfaction Estimation” | Praveen Kumar Bodigutla, Spyros Matsoukas, Lazaros Polymenakos
- “Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answering” | Li Zhou, Kevin Small
- Science Meets Engineering of Deep Learning
- "X-BERT: eXtreme Multi-label Text Classification using Bidirectional Encoder from Transformers" Wei-Cheng Chang, Hsiang-Fu Yu, Kai Zhong, Yiming Yang, Inderjit S. Dhillon
- Machine Learning with Guarantees
- Organizers: Ben London, Thorsten Joachims
- Program Committee: Kevin Small, Shiva Kasiviswanathan, Ted Sandler
- MLSys: Workshop on Systems for ML
- "Block-distributed Gradient Boosted Trees" | Theodore Vasiloudis, Hyunsu Cho, Henrik Boström
Internships for PhD Students
We offer 3-6 month internships year-round, with opportunities in Aachen, Atlanta, Austin, Bangalore, Barcelona, Berlin, Boston, Cambridge, Cupertino, Graz, Haifa, Herzliya, Manhattan Beach, New York, Palo Alto, Pasadena, Pittsburgh, San Francisco, Shanghai, Seattle, Sunnyvale, Tel Aviv, Tübingen, Turin, and Vancouver. To apply, email your resume to NeurIPS2019@amazon.com, and let us know if there are any specific locations, teams, or research leaders that you are interested in working with.
Job Opportunities for Graduating Students and Experienced Researchers
We are looking for results-driven individuals who apply advanced computer vision and machine learning techniques, love to work with data, are deeply technical, and highly innovative. If you long for the opportunity to invent and build solutions to challenging problems that directly impact the way Amazon transforms the consumer experience, we are the place for you. To apply, email your resume to NeurIPS2019@amazon.com and let us know if there are any specific locations, teams, or research leaders that you are interested in working with.
Amazon Scholars is a new program for academic leaders to work with Amazon in a flexible capacity, ranging from part-time to full-time research roles. Learn more at amazon.jobs/scholars.
BAIR Lab Opening
Amazon & the University of California Berkeley ARtificial Intelligence Research (BAIR) Lab Partnered to open the BAIR Open Research Commons, a new industrial affiliate program launched to accelerate cutting-edge AI research. The BAIR Commons is designed to streamline collaborative, cutting-edge research by students, faculty, and corporate research scholars.
Amazon and NSF Collaborate to Accelerate Fairness in AI Research
NSF and Amazon are partnering to jointly support computational research focused on fairness in AI, with the goal of contributing to trustworthy AI systems that are readily accepted and deployed to tackle grand challenges facing society. NSF has long supported transformative research in artificial intelligence (AI) and machine learning (ML). The resulting innovations offer new levels of economic opportunity and growth, safety and security, and health and wellness.
Check out the details here.
Amazon Web Services (AWS) Research Grants
In partnership with Machine Learning@Amazon, AWS offers up to $20,000 in compute tokens each quarter to professors and students. Academics have used these grants for projects ranging from Hack End weekends to massive MRI imaging projects. AWS provides building blocks for developing applications ranging from Elastic MapReduce for Hadoop analytics to fast and scalable storage with Amazon DynamoDB. Learn more & apply here.
Amazon Research Awards
ARA is an unrestricted gift to recognize exceptional faculty, and fund projects leading toward a PhD degree or conducted as a part of post-doctoral work. Each selected proposal is assigned an Amazon research contact, as we believe that both sides benefit from direct interaction on the topic of their research. We invite ARA recipients to visit Amazon offices worldwide to give talks related to their work and meet with our research groups face-to-face. We encourage ARA recipients to publish the outcome of the project and commit any related code to open source code repositories. Learn more here.
Applications will be open on September 10th, 2019 with a submission deadline of October 4, 2019.
- Computer vision
- Fairness in artificial intelligence
- Knowledge management and data quality
- Machine learning algorithms and theory
- Natural language processing
- Online advertising
- Operations research and optimization
- Search and information retrieval
- Security, privacy and abuse prevention
Publishing at Amazon
Amazon is committed to innovating at the frontiers of machine learning and artificial intelligence. Our scientists are encouraged to engage in the research community in the form of written publications, open source code and public datasets. We have instituted a new, fast-track publication approval process, to help share our research efforts as quickly as possible, while maintaining the highest standards of quality. Check out some of our most recent publications here.
Diversity at Amazon
We are a company of builders working on behalf of a global customer base. Diversity is core to our leadership principles, as we seek diverse perspectives so that we can be “Right, A Lot”. We welcome people from all backgrounds and perspectives to innovate with us. Learn more at amazon.com/diversity.
Questions about career opportunities or academic partnerships? Contact us at NeurIPS2019@amazon.com.
Learn more about Amazon's research teams:
Customer-obsessed Science at Amazon
Scientists at Amazon explain the customer-obsessed science they're tackling to bring Amazon products and services to life.
“I spoke to the future and it listened.” - Gizmodo. Meet the team of world-class scientists behind Alexa.
The history of Amazon's recommendation algorithm
Consumer division CEO, Jeff Wilke, discusses the history of Amazon's recommendation algorithm at re:MARS 2019.
Ever wonder how Amazon delivers your packages so quickly? In some cases, robots.
The Shopping Core Team
Shopping Core owns many of the technical features and functionality for Amazon.com
The Social Shopping Team
Social Shopping is responsible for our product reviews, recommendations, Wishlist and much more.