A quick guide to Amazon’s papers at NeurIPS 2022

Topics range from specific applications, such as computer vision, to more general problems, such as continual learning, to popular AI methods, such as variational autoencoders.

The Conference on Neural Information Processing Systems (NeurIPS) remains the highest-profile conference in AI, and as such, it draws paper submissions from across Amazon’s business lines. Some of those papers concern specific application areas, like computer vision and recommender systems, but many of them address more general problems, such as continual learning, federated learning, and privacy. And some of them investigate ways to improve popular machine learning methods, such as contrastive learning or variational autoencoders.

Below is a quick guide to the main-conference papers from Amazon researchers at this year’s NeurIPS.

Algorithmic fairness

Are two heads the same as one? Identifying disparate treatment in fair neural networks
Michael Lohaus, Matthaus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, Chris Russell

Computer vision

An in-depth study of stochastic backpropagation
Jun Fang, Mingze Xu, Hao Chen, Bing Shuai, Zhuowen Tu, Joseph Tighe

Self supervised amodal video object segmentation
Jian Yao, Yuxin Hong, Chiyu Wang, Tianjun Xiao, Tong He, Francesco Locatello, David Wipf, Yanwei Fu, Zheng Zhang

Self-supervised pretraining for large-scale point clouds
Zaiwei Zhang, Min Bai, Erran Li

Point clouds.png
The method described in "Self-supervised pretraining for large-scale point clouds" splits a large-scale 3-D point cloud into M occupied volumes, then subjects it to random rotations and scaling to produce two augmented views. The augmented views are then sampled to produce global and local crops.

Semi-supervised vision transformers at scale
Zhaowei Cai, Avinash Ravichandran, Paolo Favaro, Manchen Wang, Davide Modolo, Rahul Bhotika, Zhuowen Tu, Stefano Soatto

Continual learning

Measuring and reducing model update regression in structured prediction for NLP
Deng Cai, Elman Mansimov, Yi-An Lai, Yixuan Su, Lei Shu, Yi Zhang

Memory efficient continual learning with transformers
Beyza Ermis, Giovanni Zappella, Martin Wistuba, Cédric Archambeau

Distribution shifts

Assaying out-of-distribution generalization in transfer learning
Florian Wenzel, Andrea Dittadi, Peter Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Schölkopf, Francesco Locatello

Neural attentive circuits
Martin Weiss, Nasim Rahaman, Francesco Locatello, Chris Pal, Yoshua Bengio, Nicolas Ballas, Erran Li

Earth system forecasting

Earthformer: Exploring space-time transformers for earth system forecasting
Zhihan Gao, Xingjian Shi, Hao Wang, Yi Zhu, Yuyang (Bernie) Wang, Mu Li, Dit-Yan Yeung

Earth system forecasting.png
A sequence of images of a weather event captured at 10-minute intervals. Color (on a green-to-violet spectrum) indicates precipitation intensity. From "Earthformer: Exploring space-time transformers for earth system forecasting".

Federated learning

Self-aware personalized federated learning
Huili Chen, Jie Ding, Eric Tramel, Shuang Wu, Anit Kumar Sahu, Salman Avestimehr, Tao Zhang

Machine learning methods

Embrace the gap: VAEs perform independent mechanism analysis
Patrik Reizinger, Luigi Gresele, Jack Brady, Julius von Kuegelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve

Learning manifold dimensions with conditional variational autoencoders
Yijia Zheng, Tong He, Yixuan Qiu, David Wipf

On the detrimental effect of invariances in the likelihood for variational inference
Richard Kurle, Ralf Herbrich, Tim Januschowski, Yuyang (Bernie) Wang, Jan Gasthaus

Variational inference.png
In Bayesian neural networks, weights and biases are treated as random variables whose posterior distribution is induced by a dataset. The most common way to approximate the posterior is mean-field approximation, which is a product of independent normal distributions. In "On the detrimental effect of invariances in the likelihood for variational inference", the authors prove that, under the right conditions, the mean-field approximation induces the same posterior predictive distribution as an invariance-abiding approximation that explicitly models invariances.

Why do we need large batch sizes in contrastive learning? A gradient-bias perspective
Changyou Chen, Jianyi Zhang, Yi Xu, Liqun Chen, Jiali Duan, Yiran Chen, Son Tran, Belinda Zeng, Trishul Chilimbi


Private synthetic data for multitask learning and marginal queries
Giuseppe Vietri, Cédric Archambeau, Sergul Aydore, William Brown, Michael Kearns, Aaron Roth, Ankit Siva, Shuai Tang, Steven Wu

Recommender systems

Toward understanding privileged features distillation in learning-to-rank
Shuo Yang, Sujay Sanghavi, Holakou Rahmanian, Jan Bakus, S. V. N. Vishwanathan

Uplifting bandits
Yu-Guan Hsieh, Shiva Kasiviswanathan, Branislav Kveton

Reinforcement learning

Adaptive interest for emphatic reinforcement learning
Martin Klissarov, Rasool Fakoor, Jonas Mueller, Kavosh Asadi, Taesup Kim, Alex Smola

Faster deep reinforcement learning with slower online network
Kavosh Asadi, Rasool Fakoor, Omer Gottesman, Taesup Kim, Michael L. Littman, Alex Smola

Tabular data

Learning enhanced representations for tabular data via neighborhood propagation
Kounianhua Du, Weinan Zhang, Ruiwen Zhou, Yangkun Wang, Xilong Zhao, Jiarui Jin, Quan Gan, Zheng Zhang, David Paul Wipf

Tabular data.png
Unlike existing approaches to making predictions over tabular data, such as tree models (left) and retrieval methods (center), the method proposed in "Learning enhanced representations for tabular data via neighborhood propagation" models multiple data instances as a hypergraph and captures their correlations with the assistance of labels (right).

Research areas

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