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Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation | https://papers.nips.cc/paper_files/paper/2022/hash/f7fef21d1fb3e950b12b50ad7f395e31-Abstract-Conference.html | YUANWEI LIU, Nian Liu, Xiwen Yao, Junwei Han | https://papers.nips.cc/paper_files/paper/2022/hash/f7fef21d1fb3e950b12b50ad7f395e31-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18156-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/f7fef21d1fb3e950b12b50ad7f395e31-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/f7fef21d1fb3e950b12b50ad7f395e31-Supplemental-Conference.pdf | Few-shot semantic segmentation aims to segment the target objects in query under the condition of a few annotated support images. Most previous works strive to mine more effective category information from the support to match with the corresponding objects in query. However, they all ignored the category information g... | null | null |
Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning | https://papers.nips.cc/paper_files/paper/2022/hash/f8290ccc2905538be1a7f7914ccef629-Abstract-Conference.html | Yuchong Sun, Hongwei Xue, Ruihua Song, Bei Liu, Huan Yang, Jianlong Fu | https://papers.nips.cc/paper_files/paper/2022/hash/f8290ccc2905538be1a7f7914ccef629-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19099-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/f8290ccc2905538be1a7f7914ccef629-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/f8290ccc2905538be1a7f7914ccef629-Supplemental-Conference.pdf | Large-scale video-language pre-training has shown significant improvement in video-language understanding tasks. Previous studies of video-language pretraining mainly focus on short-form videos (i.e., within 30 seconds) and sentences, leaving long-form video-language pre-training rarely explored. Directly learning repr... | null | null |
Model Preserving Compression for Neural Networks | https://papers.nips.cc/paper_files/paper/2022/hash/f8928b073ccbec15d35f2a9d39430bfd-Abstract-Conference.html | Jerry Chee, Megan Flynn (née Renz), Anil Damle, Christopher M. De Sa | https://papers.nips.cc/paper_files/paper/2022/hash/f8928b073ccbec15d35f2a9d39430bfd-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17664-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/f8928b073ccbec15d35f2a9d39430bfd-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/f8928b073ccbec15d35f2a9d39430bfd-Supplemental-Conference.zip | After training complex deep learning models, a common task is to compress the model to reduce compute and storage demands. When compressing, it is desirable to preserve the original model's per-example decisions (e.g., to go beyond top-1 accuracy or preserve robustness), maintain the network's structure, automatically ... | null | null |
Neural Conservation Laws: A Divergence-Free Perspective | https://papers.nips.cc/paper_files/paper/2022/hash/f8d39584f87944e5dbe46ec76f19e20a-Abstract-Conference.html | Jack Richter-Powell, Yaron Lipman, Ricky T. Q. Chen | https://papers.nips.cc/paper_files/paper/2022/hash/f8d39584f87944e5dbe46ec76f19e20a-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17350-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/f8d39584f87944e5dbe46ec76f19e20a-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/f8d39584f87944e5dbe46ec76f19e20a-Supplemental-Conference.pdf | We investigate the parameterization of deep neural networks that by design satisfy the continuity equation, a fundamental conservation law. This is enabled by the observation that any solution of the continuity equation can be represented as a divergence-free vector field. We hence propose building divergence-free neur... | null | null |
Towards Effective Multi-Modal Interchanges in Zero-Resource Sounding Object Localization | https://papers.nips.cc/paper_files/paper/2022/hash/f8de10c9ff056ae3d1eef43ad1762351-Abstract-Conference.html | Yang Zhao, Chen Zhang, Haifeng Huang, Haoyuan Li, Zhou Zhao | https://papers.nips.cc/paper_files/paper/2022/hash/f8de10c9ff056ae3d1eef43ad1762351-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17142-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/f8de10c9ff056ae3d1eef43ad1762351-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/f8de10c9ff056ae3d1eef43ad1762351-Supplemental-Conference.pdf | Aiming to locate the object that emits a specified sound in complex scenes, the task of sounding object localization bridges two perception-oriented modalities of vision and acoustics, and brings enormous research value to the comprehensive perceptual understanding of machine intelligence. Although there are massive tr... | null | null |
On the Convergence of Stochastic Multi-Objective Gradient Manipulation and Beyond | https://papers.nips.cc/paper_files/paper/2022/hash/f91bd64a3620aad8e70a27ad9cb3ca57-Abstract-Conference.html | Shiji Zhou, Wenpeng Zhang, Jiyan Jiang, Wenliang Zhong, Jinjie GU, Wenwu Zhu | https://papers.nips.cc/paper_files/paper/2022/hash/f91bd64a3620aad8e70a27ad9cb3ca57-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18586-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/f91bd64a3620aad8e70a27ad9cb3ca57-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/f91bd64a3620aad8e70a27ad9cb3ca57-Supplemental-Conference.pdf | The conflicting gradients problem is one of the major bottlenecks for the effective training of machine learning models that deal with multiple objectives. To resolve this problem, various gradient manipulation techniques, such as PCGrad, MGDA, and CAGrad, have been developed, which directly alter the conflicting gradi... | null | null |
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities | https://papers.nips.cc/paper_files/paper/2022/hash/f9379afacdbabfdc6b060972b60f9ab8-Abstract-Conference.html | Aleksandr Beznosikov, Pavel Dvurechenskii, Anastasiia Koloskova, Valentin Samokhin, Sebastian U. Stich, Alexander Gasnikov | https://papers.nips.cc/paper_files/paper/2022/hash/f9379afacdbabfdc6b060972b60f9ab8-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17401-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/f9379afacdbabfdc6b060972b60f9ab8-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/f9379afacdbabfdc6b060972b60f9ab8-Supplemental-Conference.pdf | We consider distributed stochastic variational inequalities (VIs) on unbounded domains with the problem data that is heterogeneous (non-IID) and distributed across many devices. We make a very general assumption on the computational network that, in particular, covers the settings of fully decentralized calculations wi... | null | null |
Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation | https://papers.nips.cc/paper_files/paper/2022/hash/f959b05dd74ba8a735276c3df4ae8b71-Abstract-Conference.html | Peihao Chen, Dongyu Ji, Kunyang Lin, Runhao Zeng, Thomas Li, Mingkui Tan, Chuang Gan | https://papers.nips.cc/paper_files/paper/2022/hash/f959b05dd74ba8a735276c3df4ae8b71-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18278-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/f959b05dd74ba8a735276c3df4ae8b71-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/f959b05dd74ba8a735276c3df4ae8b71-Supplemental-Conference.pdf | We address a practical yet challenging problem of training robot agents to navigate in an environment following a path described by some language instructions. The instructions often contain descriptions of objects in the environment. To achieve accurate and efficient navigation, it is critical to build a map that accu... | null | null |
Thinned random measures for sparse graphs with overlapping communities | https://papers.nips.cc/paper_files/paper/2022/hash/f9668d223e713943634dce9c66e8f2c1-Abstract-Conference.html | Federica Zoe Ricci, Michele Guindani, Erik Sudderth | https://papers.nips.cc/paper_files/paper/2022/hash/f9668d223e713943634dce9c66e8f2c1-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18308-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/f9668d223e713943634dce9c66e8f2c1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/f9668d223e713943634dce9c66e8f2c1-Supplemental-Conference.pdf | Network models for exchangeable arrays, including most stochastic block models, generate dense graphs with a limited ability to capture many characteristics of real-world social and biological networks. A class of models based on completely random measures like the generalized gamma process (GGP) have recently addresse... | null | null |
Fine-tuning language models to find agreement among humans with diverse preferences | https://papers.nips.cc/paper_files/paper/2022/hash/f978c8f3b5f399cae464e85f72e28503-Abstract-Conference.html | Michiel Bakker, Martin Chadwick, Hannah Sheahan, Michael Tessler, Lucy Campbell-Gillingham, Jan Balaguer, Nat McAleese, Amelia Glaese, John Aslanides, Matt Botvinick, Christopher Summerfield | https://papers.nips.cc/paper_files/paper/2022/hash/f978c8f3b5f399cae464e85f72e28503-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17047-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/f978c8f3b5f399cae464e85f72e28503-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/f978c8f3b5f399cae464e85f72e28503-Supplemental-Conference.pdf | Recent work in large language modeling (LLMs) has used fine-tuning to align outputs with the preferences of a prototypical user. This work assumes that human preferences are static and homogeneous across individuals, so that aligning to a single "generic" user will confer more general alignment. Here, we embrace the he... | null | null |
What is a Good Metric to Study Generalization of Minimax Learners? | https://papers.nips.cc/paper_files/paper/2022/hash/f9b8853ea81731f9bfc11820b064de96-Abstract-Conference.html | Asuman Ozdaglar, Sarath Pattathil, Jiawei Zhang, Kaiqing Zhang | https://papers.nips.cc/paper_files/paper/2022/hash/f9b8853ea81731f9bfc11820b064de96-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18527-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/f9b8853ea81731f9bfc11820b064de96-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/f9b8853ea81731f9bfc11820b064de96-Supplemental-Conference.pdf | Minimax optimization has served as the backbone of many machine learning problems. Although the convergence behavior of optimization algorithms has been extensively studied in minimax settings, their generalization guarantees, i.e., how the model trained on empirical data performs on the unseen testing data, have been ... | null | null |
Sequencer: Deep LSTM for Image Classification | https://papers.nips.cc/paper_files/paper/2022/hash/f9d7d6c695bc983fcfb5b70a5fbdfd2f-Abstract-Conference.html | Yuki Tatsunami, Masato Taki | https://papers.nips.cc/paper_files/paper/2022/hash/f9d7d6c695bc983fcfb5b70a5fbdfd2f-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17888-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/f9d7d6c695bc983fcfb5b70a5fbdfd2f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/f9d7d6c695bc983fcfb5b70a5fbdfd2f-Supplemental-Conference.pdf | In recent computer vision research, the advent of the Vision Transformer (ViT) has rapidly revolutionized various architectural design efforts: ViT achieved state-of-the-art image classification performance using self-attention found in natural language processing, and MLP-Mixer achieved competitive performance using s... | null | null |
Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imagination | https://papers.nips.cc/paper_files/paper/2022/hash/f9e2800a251fa9107a008104f47c45d1-Abstract-Conference.html | Jiafei Lyu, Xiu Li, Zongqing Lu | https://papers.nips.cc/paper_files/paper/2022/hash/f9e2800a251fa9107a008104f47c45d1-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17600-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/f9e2800a251fa9107a008104f47c45d1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/f9e2800a251fa9107a008104f47c45d1-Supplemental-Conference.pdf | The learned policy of model-free offline reinforcement learning (RL) methods is often constrained to stay within the support of datasets to avoid possible dangerous out-of-distribution actions or states, making it challenging to handle out-of-support region. Model-based RL methods offer a richer dataset and benefit gen... | null | null |
Learning on Arbitrary Graph Topologies via Predictive Coding | https://papers.nips.cc/paper_files/paper/2022/hash/f9f54762cbb4fe4dbffdd4f792c31221-Abstract-Conference.html | Tommaso Salvatori, Luca Pinchetti, Beren Millidge, Yuhang Song, Tianyi Bao, Rafal Bogacz, Thomas Lukasiewicz | https://papers.nips.cc/paper_files/paper/2022/hash/f9f54762cbb4fe4dbffdd4f792c31221-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19274-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/f9f54762cbb4fe4dbffdd4f792c31221-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/f9f54762cbb4fe4dbffdd4f792c31221-Supplemental-Conference.pdf | Training with backpropagation (BP) in standard deep learning consists of two main steps: a forward pass that maps a data point to its prediction, and a backward pass that propagates the error of this prediction back through the network. This process is highly effective when the goal is to minimize a specific objective ... | null | null |
Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class | https://papers.nips.cc/paper_files/paper/2022/hash/fa0126bb7ebad258bf4ffdbbac2dd787-Abstract-Conference.html | Khoa D Doan, Yingjie Lao, Ping Li | https://papers.nips.cc/paper_files/paper/2022/hash/fa0126bb7ebad258bf4ffdbbac2dd787-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17389-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fa0126bb7ebad258bf4ffdbbac2dd787-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fa0126bb7ebad258bf4ffdbbac2dd787-Supplemental-Conference.pdf | In recent years, machine learning models have been shown to be vulnerable to backdoor attacks. Under such attacks, an adversary embeds a stealthy backdoor into the trained model such that the compromised models will behave normally on clean inputs but will misclassify according to the adversary's control on maliciously... | null | null |
Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models | https://papers.nips.cc/paper_files/paper/2022/hash/fa0509f4dab6807e2cb465715bf2d249-Abstract-Conference.html | Kushal Tirumala, Aram Markosyan, Luke Zettlemoyer, Armen Aghajanyan | https://papers.nips.cc/paper_files/paper/2022/hash/fa0509f4dab6807e2cb465715bf2d249-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18962-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fa0509f4dab6807e2cb465715bf2d249-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fa0509f4dab6807e2cb465715bf2d249-Supplemental-Conference.pdf | Despite their wide adoption, the underlying training and memorization dynamics of very large language models is not well understood. We empirically study exact memorization in causal and masked language modeling, across model sizes and throughout the training process. We measure the effects of dataset size, learning ra... | null | null |
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization | https://papers.nips.cc/paper_files/paper/2022/hash/fa3c139cf8084de7bfd944f1c90c8695-Abstract-Conference.html | Hui Yuan, Chengzhuo Ni, Huazheng Wang, Xuezhou Zhang, Le Cong, Csaba Szepesvari, Mengdi Wang | https://papers.nips.cc/paper_files/paper/2022/hash/fa3c139cf8084de7bfd944f1c90c8695-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18493-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fa3c139cf8084de7bfd944f1c90c8695-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fa3c139cf8084de7bfd944f1c90c8695-Supplemental-Conference.pdf | Directed Evolution (DE), a landmark wet-lab method originated in 1960s, enables discovery of novel protein designs via evolving a population of candidate sequences. Recent advances in biotechnology has made it possible to collect high-throughput data, allowing the use of machine learning to map out a protein's sequence... | null | null |
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy | https://papers.nips.cc/paper_files/paper/2022/hash/fa5617c176e76fee83f3f9947fdf9f3f-Abstract-Conference.html | Zhiqi Bu, Jialin Mao, Shiyun Xu | https://papers.nips.cc/paper_files/paper/2022/hash/fa5617c176e76fee83f3f9947fdf9f3f-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18675-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fa5617c176e76fee83f3f9947fdf9f3f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fa5617c176e76fee83f3f9947fdf9f3f-Supplemental-Conference.zip | Large convolutional neural networks (CNN) can be difficult to train in the differentially private (DP) regime, since the optimization algorithms require a computationally expensive operation, known as the per-sample gradient clipping. We propose an efficient and scalable implementation of this clipping on convolutional... | null | null |
Weakly supervised causal representation learning | https://papers.nips.cc/paper_files/paper/2022/hash/fa567e2b2c870f8f09a87b6e73370869-Abstract-Conference.html | Johann Brehmer, Pim de Haan, Phillip Lippe, Taco S. Cohen | https://papers.nips.cc/paper_files/paper/2022/hash/fa567e2b2c870f8f09a87b6e73370869-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17115-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fa567e2b2c870f8f09a87b6e73370869-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fa567e2b2c870f8f09a87b6e73370869-Supplemental-Conference.pdf | Learning high-level causal representations together with a causal model from unstructured low-level data such as pixels is impossible from observational data alone. We prove under mild assumptions that this representation is however identifiable in a weakly supervised setting. This involves a dataset with paired sample... | null | null |
Zeroth-Order Negative Curvature Finding: Escaping Saddle Points without Gradients | https://papers.nips.cc/paper_files/paper/2022/hash/fa5ddd6bac0d665c72969d79221b680a-Abstract-Conference.html | Hualin Zhang, Huan Xiong, Bin Gu | https://papers.nips.cc/paper_files/paper/2022/hash/fa5ddd6bac0d665c72969d79221b680a-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19301-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fa5ddd6bac0d665c72969d79221b680a-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fa5ddd6bac0d665c72969d79221b680a-Supplemental-Conference.zip | We consider escaping saddle points of nonconvex problems where only the function evaluations can be accessed. Although a variety of works have been proposed, the majority of them require either second or first-order information, and only a few of them have exploited zeroth-order methods, particularly the technique of n... | null | null |
Exposing and Exploiting Fine-Grained Block Structures for Fast and Accurate Sparse Training | https://papers.nips.cc/paper_files/paper/2022/hash/fa69e968b7319fd42524febd41475fb3-Abstract-Conference.html | Peng Jiang, Lihan Hu, Shihui Song | https://papers.nips.cc/paper_files/paper/2022/hash/fa69e968b7319fd42524febd41475fb3-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16725-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fa69e968b7319fd42524febd41475fb3-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fa69e968b7319fd42524febd41475fb3-Supplemental-Conference.pdf | Sparse training is a popular technique to reduce the overhead of training large models. Although previous work has shown promising results for nonstructured sparse models, it is still unclear whether a sparse model with structural constraints can be trained from scratch to high accuracy. In this work, we study the dyna... | null | null |
Operator Splitting Value Iteration | https://papers.nips.cc/paper_files/paper/2022/hash/fa809df3ec53cc5781e5078b7d500a5d-Abstract-Conference.html | Amin Rakhsha, Andrew Wang, Mohammad Ghavamzadeh, Amir-massoud Farahmand | https://papers.nips.cc/paper_files/paper/2022/hash/fa809df3ec53cc5781e5078b7d500a5d-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17866-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fa809df3ec53cc5781e5078b7d500a5d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fa809df3ec53cc5781e5078b7d500a5d-Supplemental-Conference.zip | We introduce new planning and reinforcement learning algorithms for discounted MDPs that utilize an approximate model of the environment to accelerate the convergence of the value function. Inspired by the splitting approach in numerical linear algebra, we introduce \emph{Operator Splitting Value Iteration} (OS-VI) for... | null | null |
Enhanced Latent Space Blind Model for Real Image Denoising via Alternative Optimization | https://papers.nips.cc/paper_files/paper/2022/hash/fa93d7bfb48450e1af63c8fa647d317f-Abstract-Conference.html | Chao Ren, Yizhong Pan, Jie Huang | https://papers.nips.cc/paper_files/paper/2022/hash/fa93d7bfb48450e1af63c8fa647d317f-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16761-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fa93d7bfb48450e1af63c8fa647d317f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fa93d7bfb48450e1af63c8fa647d317f-Supplemental-Conference.zip | Motivated by the achievements in model-based methods and the advances in deep networks, we propose a novel enhanced latent space blind model based deep unfolding network, namely ScaoedNet, for complex real image denoising. It is derived by introducing latent space, noise information, and guidance constraint into the de... | null | null |
Self-Explaining Deviations for Coordination | https://papers.nips.cc/paper_files/paper/2022/hash/faa6276ea12d7afeb3e42b210c86f688-Abstract-Conference.html | Hengyuan Hu, Samuel Sokota, David Wu, Anton Bakhtin, Andrei Lupu, Brandon Cui, Jakob Foerster | https://papers.nips.cc/paper_files/paper/2022/hash/faa6276ea12d7afeb3e42b210c86f688-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19114-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/faa6276ea12d7afeb3e42b210c86f688-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/faa6276ea12d7afeb3e42b210c86f688-Supplemental-Conference.pdf | Fully cooperative, partially observable multi-agent problems are ubiquitous in the real world. In this paper, we focus on a specific subclass of coordination problems in which humans are able to discover self-explaining deviations (SEDs). SEDs are actions that deviate from the common understanding of what reasonable be... | null | null |
Communication Efficient Federated Learning for Generalized Linear Bandits | https://papers.nips.cc/paper_files/paper/2022/hash/faa8be9311811ba7c36fa1ceec13b862-Abstract-Conference.html | Chuanhao Li, Hongning Wang | https://papers.nips.cc/paper_files/paper/2022/hash/faa8be9311811ba7c36fa1ceec13b862-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18737-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/faa8be9311811ba7c36fa1ceec13b862-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/faa8be9311811ba7c36fa1ceec13b862-Supplemental-Conference.zip | Contextual bandit algorithms have been recently studied under the federated learning setting to satisfy the demand of keeping data decentralized and pushing the learning of bandit models to the client side. But limited by the required communication efficiency, existing solutions are restricted to linear models to explo... | null | null |
Active Learning for Multiple Target Models | https://papers.nips.cc/paper_files/paper/2022/hash/faacb7a4827b4d51e201666b93ab5fa7-Abstract-Conference.html | Ying-Peng Tang, Sheng-Jun Huang | https://papers.nips.cc/paper_files/paper/2022/hash/faacb7a4827b4d51e201666b93ab5fa7-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19171-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/faacb7a4827b4d51e201666b93ab5fa7-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/faacb7a4827b4d51e201666b93ab5fa7-Supplemental-Conference.pdf | We describe and explore a novel setting of active learning (AL), where there are multiple target models to be learned simultaneously. In many real applications, the machine learning system is required to be deployed on diverse devices with varying computational resources (e.g., workstation, mobile phone, edge devices, ... | null | null |
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks | https://papers.nips.cc/paper_files/paper/2022/hash/facaa170287a034cf99cf0489a7f8430-Abstract-Conference.html | Hongjoon Ahn, Yongyi Yang, Quan Gan, Taesup Moon, David P Wipf | https://papers.nips.cc/paper_files/paper/2022/hash/facaa170287a034cf99cf0489a7f8430-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18088-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/facaa170287a034cf99cf0489a7f8430-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/facaa170287a034cf99cf0489a7f8430-Supplemental-Conference.pdf | Heterogeneous graph neural networks (GNNs) achieve strong performance on node classification tasks in a semi-supervised learning setting. However, as in the simpler homogeneous GNN case, message-passing-based heterogeneous GNNs may struggle to balance between resisting the oversmoothing that may occur in deep models, a... | null | null |
Multi-agent Performative Prediction with Greedy Deployment and Consensus Seeking Agents | https://papers.nips.cc/paper_files/paper/2022/hash/fad7c708dda11f3e72cc1629bb130379-Abstract-Conference.html | Qiang LI, Chung-Yiu Yau, Hoi-To Wai | https://papers.nips.cc/paper_files/paper/2022/hash/fad7c708dda11f3e72cc1629bb130379-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17880-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fad7c708dda11f3e72cc1629bb130379-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fad7c708dda11f3e72cc1629bb130379-Supplemental-Conference.pdf | We consider a scenario where multiple agents are learning a common decision vector from data which can be influenced by the agents’ decisions. This leads to the problem of multi-agent performative prediction (Multi-PfD). In this paper, we formulate Multi-PfD as a decentralized optimization problem that minimizes a sum ... | null | null |
Preservation of the Global Knowledge by Not-True Distillation in Federated Learning | https://papers.nips.cc/paper_files/paper/2022/hash/fadec8f2e65f181d777507d1df69b92f-Abstract-Conference.html | Gihun Lee, Minchan Jeong, Yongjin Shin, Sangmin Bae, Se-Young Yun | https://papers.nips.cc/paper_files/paper/2022/hash/fadec8f2e65f181d777507d1df69b92f-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19038-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fadec8f2e65f181d777507d1df69b92f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fadec8f2e65f181d777507d1df69b92f-Supplemental-Conference.pdf | In federated learning, a strong global model is collaboratively learned by aggregating clients' locally trained models. Although this precludes the need to access clients' data directly, the global model's convergence often suffers from data heterogeneity. This study starts from an analogy to continual learning and sug... | null | null |
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits | https://papers.nips.cc/paper_files/paper/2022/hash/fb23cf87a9e04d7677b73c47acd060ef-Abstract-Conference.html | Tianyuan Jin, Pan Xu, Xiaokui Xiao, Anima Anandkumar | https://papers.nips.cc/paper_files/paper/2022/hash/fb23cf87a9e04d7677b73c47acd060ef-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18634-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fb23cf87a9e04d7677b73c47acd060ef-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fb23cf87a9e04d7677b73c47acd060ef-Supplemental-Conference.pdf | We study the regret of Thompson sampling (TS) algorithms for exponential family bandits, where the reward distribution is from a one-dimensional exponential family, which covers many common reward distributions including Bernoulli, Gaussian, Gamma, Exponential, etc. We propose a Thompson sampling algorithm, termed ExpT... | null | null |
Graph Reordering for Cache-Efficient Near Neighbor Search | https://papers.nips.cc/paper_files/paper/2022/hash/fb44a668c2d4bc984e9d6ca261262cbb-Abstract-Conference.html | Benjamin Coleman, Santiago Segarra, Alexander J. Smola, Anshumali Shrivastava | https://papers.nips.cc/paper_files/paper/2022/hash/fb44a668c2d4bc984e9d6ca261262cbb-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16990-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fb44a668c2d4bc984e9d6ca261262cbb-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fb44a668c2d4bc984e9d6ca261262cbb-Supplemental-Conference.zip | Graph search is one of the most successful algorithmic trends in near neighbor search. Several of the most popular and empirically successful algorithms are, at their core, a greedy walk along a pruned near neighbor graph. However, graph traversal applications often suffer from poor memory access patterns, and near nei... | null | null |
MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning | https://papers.nips.cc/paper_files/paper/2022/hash/fb575ab4d882a4c734641155a5f30911-Abstract-Conference.html | Jiangmeng Li, Wenwen Qiang, Yanan Zhang, Wenyi Mo, Changwen Zheng, Bing Su, Hui Xiong | https://papers.nips.cc/paper_files/paper/2022/hash/fb575ab4d882a4c734641155a5f30911-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18670-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fb575ab4d882a4c734641155a5f30911-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fb575ab4d882a4c734641155a5f30911-Supplemental-Conference.pdf | As a successful approach to self-supervised learning, contrastive learning aims to learn invariant information shared among distortions of the input sample. While contrastive learning has yielded continuous advancements in sampling strategy and architecture design, it still remains two persistent defects: the interfere... | null | null |
On Feature Learning in the Presence of Spurious Correlations | https://papers.nips.cc/paper_files/paper/2022/hash/fb64a552feda3d981dbe43527a80a07e-Abstract-Conference.html | Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew G. Wilson | https://papers.nips.cc/paper_files/paper/2022/hash/fb64a552feda3d981dbe43527a80a07e-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19262-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fb64a552feda3d981dbe43527a80a07e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fb64a552feda3d981dbe43527a80a07e-Supplemental-Conference.pdf | Deep classifiers are known to rely on spurious features — patterns which are correlated with the target on the training data but not inherently relevant to the learning problem, such as the image backgrounds when classifying the foregrounds. In this paper we evaluate the amount of information about the core (non-spurio... | null | null |
Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection | https://papers.nips.cc/paper_files/paper/2022/hash/fb71332951af4ae27fbd457daadc5341-Abstract-Conference.html | Tianyu Wang, Xiaowei Hu, Zhengzhe LIU, Chi-Wing Fu | https://papers.nips.cc/paper_files/paper/2022/hash/fb71332951af4ae27fbd457daadc5341-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17960-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fb71332951af4ae27fbd457daadc5341-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fb71332951af4ae27fbd457daadc5341-Supplemental-Conference.pdf | LiDAR-produced point clouds are the major source for most state-of-the-art 3D object detectors. Yet, small, distant, and incomplete objects with sparse or few points are often hard to detect. We present Sparse2Dense, a new framework to efficiently boost 3D detection performance by learning to densify point clouds in la... | null | null |
Exploring Length Generalization in Large Language Models | https://papers.nips.cc/paper_files/paper/2022/hash/fb7451e43f9c1c35b774bcfad7a5714b-Abstract-Conference.html | Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, Behnam Neyshabur | https://papers.nips.cc/paper_files/paper/2022/hash/fb7451e43f9c1c35b774bcfad7a5714b-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18909-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fb7451e43f9c1c35b774bcfad7a5714b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fb7451e43f9c1c35b774bcfad7a5714b-Supplemental-Conference.pdf | The ability to extrapolate from short problem instances to longer ones is an important form of out-of-distribution generalization in reasoning tasks, and is crucial when learning from datasets where longer problem instances are rare. These include theorem proving, solving quantitative mathematics problems, and reading/... | null | null |
Stability and Generalization Analysis of Gradient Methods for Shallow Neural Networks | https://papers.nips.cc/paper_files/paper/2022/hash/fb8fe6b79288f3d83696a5d276f4fc9d-Abstract-Conference.html | Yunwen Lei, Rong Jin, Yiming Ying | https://papers.nips.cc/paper_files/paper/2022/hash/fb8fe6b79288f3d83696a5d276f4fc9d-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19188-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fb8fe6b79288f3d83696a5d276f4fc9d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fb8fe6b79288f3d83696a5d276f4fc9d-Supplemental-Conference.pdf | While significant theoretical progress has been achieved, unveiling the generalization mystery of overparameterized neural networks still remains largely elusive. In this paper, we study the generalization behavior of shallow neural networks (SNNs) by leveraging the concept of algorithmic stability. We consider gradie... | null | null |
ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation | https://papers.nips.cc/paper_files/paper/2022/hash/fbb10d319d44f8c3b4720873e4177c65-Abstract-Conference.html | Yufei Xu, Jing Zhang, Qiming ZHANG, Dacheng Tao | https://papers.nips.cc/paper_files/paper/2022/hash/fbb10d319d44f8c3b4720873e4177c65-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18599-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fbb10d319d44f8c3b4720873e4177c65-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fbb10d319d44f8c3b4720873e4177c65-Supplemental-Conference.pdf | Although no specific domain knowledge is considered in the design, plain vision transformers have shown excellent performance in visual recognition tasks. However, little effort has been made to reveal the potential of such simple structures for pose estimation tasks. In this paper, we show the surprisingly good capabi... | null | null |
Re-Analyze Gauss: Bounds for Private Matrix Approximation via Dyson Brownian Motion | https://papers.nips.cc/paper_files/paper/2022/hash/fbc9981dd6316378aee7fd5975250f21-Abstract-Conference.html | Oren Mangoubi, Nisheeth Vishnoi | https://papers.nips.cc/paper_files/paper/2022/hash/fbc9981dd6316378aee7fd5975250f21-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17060-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fbc9981dd6316378aee7fd5975250f21-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fbc9981dd6316378aee7fd5975250f21-Supplemental-Conference.zip | Given a symmetric matrix $M$ and a vector $\lambda$, we present new bounds on the Frobenius-distance utility of the Gaussian mechanism for approximating $M$ by a matrix whose spectrum is $\lambda$, under $(\varepsilon,\delta)$-differential privacy. Our bounds depend on both $\lambda$ and the gaps in the eigenvalues of... | null | null |
ASPiRe: Adaptive Skill Priors for Reinforcement Learning | https://papers.nips.cc/paper_files/paper/2022/hash/fbd8e65962da06f83f3f28b52774ffd0-Abstract-Conference.html | Mengda Xu, Manuela Veloso, Shuran Song | https://papers.nips.cc/paper_files/paper/2022/hash/fbd8e65962da06f83f3f28b52774ffd0-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16967-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fbd8e65962da06f83f3f28b52774ffd0-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fbd8e65962da06f83f3f28b52774ffd0-Supplemental-Conference.pdf | We introduce ASPiRe (Adaptive Skill Prior for RL), a new approach that leverages prior experience to accelerate reinforcement learning. Unlike existing methods that learn a single skill prior from a large and diverse dataset, our framework learns a library of different distinction skill priors (i.e., behavior priors) f... | null | null |
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules | https://papers.nips.cc/paper_files/paper/2022/hash/fc09b26b85ab3abb2832bd555a2e4215-Abstract-Conference.html | Kazuki Irie, Francesco Faccio, Jürgen Schmidhuber | https://papers.nips.cc/paper_files/paper/2022/hash/fc09b26b85ab3abb2832bd555a2e4215-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19423-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fc09b26b85ab3abb2832bd555a2e4215-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fc09b26b85ab3abb2832bd555a2e4215-Supplemental-Conference.zip | Neural ordinary differential equations (ODEs) have attracted much attention as continuous-time counterparts of deep residual neural networks (NNs), and numerous extensions for recurrent NNs have been proposed. Since the 1980s, ODEs have also been used to derive theoretical results for NN learning rules, e.g., the famou... | null | null |
MEMO: Test Time Robustness via Adaptation and Augmentation | https://papers.nips.cc/paper_files/paper/2022/hash/fc28053a08f59fccb48b11f2e31e81c7-Abstract-Conference.html | Marvin Zhang, Sergey Levine, Chelsea Finn | https://papers.nips.cc/paper_files/paper/2022/hash/fc28053a08f59fccb48b11f2e31e81c7-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18576-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fc28053a08f59fccb48b11f2e31e81c7-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fc28053a08f59fccb48b11f2e31e81c7-Supplemental-Conference.pdf | While deep neural networks can attain good accuracy on in-distribution test points, many applications require robustness even in the face of unexpected perturbations in the input, changes in the domain, or other sources of distribution shift. We study the problem of test time robustification, i.e., using the test input... | null | null |
Learning-Augmented Algorithms for Online Linear and Semidefinite Programming | https://papers.nips.cc/paper_files/paper/2022/hash/fc5a1845bee1f5405ef99ba25c2d44e1-Abstract-Conference.html | Elena Grigorescu, Young-San Lin, Sandeep Silwal, Maoyuan Song, Samson Zhou | https://papers.nips.cc/paper_files/paper/2022/hash/fc5a1845bee1f5405ef99ba25c2d44e1-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17498-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fc5a1845bee1f5405ef99ba25c2d44e1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fc5a1845bee1f5405ef99ba25c2d44e1-Supplemental-Conference.zip | Semidefinite programming (SDP) is a unifying framework that generalizes both linear programming and quadratically-constrained quadratic programming, while also yielding efficient solvers, both in theory and in practice. However, there exist known impossibility results for approximating the optimal solution when constr... | null | null |
Text-Adaptive Multiple Visual Prototype Matching for Video-Text Retrieval | https://papers.nips.cc/paper_files/paper/2022/hash/fc65fab891d83433bd3c8d966edde311-Abstract-Conference.html | Chengzhi Lin, Ancong Wu, Junwei Liang, Jun Zhang, Wenhang Ge, Wei-Shi Zheng, Chunhua Shen | https://papers.nips.cc/paper_files/paper/2022/hash/fc65fab891d83433bd3c8d966edde311-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16767-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fc65fab891d83433bd3c8d966edde311-Paper-Conference.pdf | null | Cross-modal retrieval between videos and texts has gained increasing interest because of the rapid emergence of videos on the web. Generally, a video contains rich instance and event information and the query text only describes a part of the information. Thus, a video can have multiple different text descriptions and... | null | null |
Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm | https://papers.nips.cc/paper_files/paper/2022/hash/fc9f83d9925e6885e8f1ae1e17b3c44b-Abstract-Conference.html | HuiYang Shao, Qianqian Xu, Zhiyong Yang, Shilong Bao, Qingming Huang | https://papers.nips.cc/paper_files/paper/2022/hash/fc9f83d9925e6885e8f1ae1e17b3c44b-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18249-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fc9f83d9925e6885e8f1ae1e17b3c44b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fc9f83d9925e6885e8f1ae1e17b3c44b-Supplemental-Conference.pdf | The Partial Area Under the ROC Curve (PAUC), typically including One-way Partial AUC (OPAUC) and Two-way Partial AUC (TPAUC), measures the average performance of a binary classifier within a specific false positive rate and/or true positive rate interval, which is a widely adopted measure when decision constraints ... | null | null |
Universality of Group Convolutional Neural Networks Based on Ridgelet Analysis on Groups | https://papers.nips.cc/paper_files/paper/2022/hash/fcc3dc27672a12510babe448d665e152-Abstract-Conference.html | Sho Sonoda, Isao Ishikawa, Masahiro Ikeda | https://papers.nips.cc/paper_files/paper/2022/hash/fcc3dc27672a12510babe448d665e152-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16723-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fcc3dc27672a12510babe448d665e152-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fcc3dc27672a12510babe448d665e152-Supplemental-Conference.pdf | We show the universality of depth-2 group convolutional neural networks (GCNNs) in a unified and constructive manner based on the ridgelet theory. Despite widespread use in applications, the approximation property of (G)CNNs has not been well investigated. The universality of (G)CNNs has been shown since the late 2010s... | null | null |
Error Correction Code Transformer | https://papers.nips.cc/paper_files/paper/2022/hash/fcd3909db30887ce1da519c4468db668-Abstract-Conference.html | Yoni Choukroun, Lior Wolf | https://papers.nips.cc/paper_files/paper/2022/hash/fcd3909db30887ce1da519c4468db668-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17673-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fcd3909db30887ce1da519c4468db668-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fcd3909db30887ce1da519c4468db668-Supplemental-Conference.pdf | Error correction code is a major part of the physical communication layer, ensuring the reliable transfer of data over noisy channels.Recently, neural decoders were shown to outperform classical decoding techniques.However, the existing neural approaches present strong overfitting, due to the exponential training compl... | null | null |
Towards Improving Calibration in Object Detection Under Domain Shift | https://papers.nips.cc/paper_files/paper/2022/hash/fcd812a51b8f8d05cfea22e3c9c4b369-Abstract-Conference.html | Muhammad Akhtar Munir, Muhammad Haris Khan, M. Sarfraz, Mohsen Ali | https://papers.nips.cc/paper_files/paper/2022/hash/fcd812a51b8f8d05cfea22e3c9c4b369-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17996-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fcd812a51b8f8d05cfea22e3c9c4b369-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fcd812a51b8f8d05cfea22e3c9c4b369-Supplemental-Conference.pdf | With deep neural network based solution more readily being incorporated in real-world applications, it has been pressing requirement that predictions by such models, especially in safety-critical environments, be highly accurate and well-calibrated. Although some techniques addressing DNN calibration have been propose... | null | null |
Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning | https://papers.nips.cc/paper_files/paper/2022/hash/fcdffb372c9fa2ce757cf457415c7aab-Abstract-Conference.html | Jiachen T. Wang, Saeed Mahloujifar, Shouda Wang, Ruoxi Jia, Prateek Mittal | https://papers.nips.cc/paper_files/paper/2022/hash/fcdffb372c9fa2ce757cf457415c7aab-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16835-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fcdffb372c9fa2ce757cf457415c7aab-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fcdffb372c9fa2ce757cf457415c7aab-Supplemental-Conference.zip | Propose-Test-Release (PTR) is a differential privacy framework that works with local sensitivity of functions, instead of their global sensitivity. This framework is typically used for releasing robust statistics such as median or trimmed mean in a differentially private manner. While PTR is a common framework introduc... | null | null |
A Transformer-Based Object Detector with Coarse-Fine Crossing Representations | https://papers.nips.cc/paper_files/paper/2022/hash/fcfad93e2f30ab4c22f9ec5edfbb5cc0-Abstract-Conference.html | Zhishan Li, Ying Nie, Kai Han, Jianyuan Guo, Lei Xie, Yunhe Wang | https://papers.nips.cc/paper_files/paper/2022/hash/fcfad93e2f30ab4c22f9ec5edfbb5cc0-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18558-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fcfad93e2f30ab4c22f9ec5edfbb5cc0-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fcfad93e2f30ab4c22f9ec5edfbb5cc0-Supplemental-Conference.pdf | Transformer-based object detectors have shown competitive performance recently. Compared with convolutional neural networks limited by the relatively small receptive fields, the advantage of transformer for visual tasks is the capacity to perceive long-range dependencies among all image patches, while the deficiency i... | null | null |
Beyond Adult and COMPAS: Fair Multi-Class Prediction via Information Projection | https://papers.nips.cc/paper_files/paper/2022/hash/fd5013ea0c3f96931dec77174eaf9d80-Abstract-Conference.html | Wael Alghamdi, Hsiang Hsu, Haewon Jeong, Hao Wang, Peter Michalak, Shahab Asoodeh, Flavio Calmon | https://papers.nips.cc/paper_files/paper/2022/hash/fd5013ea0c3f96931dec77174eaf9d80-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17444-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fd5013ea0c3f96931dec77174eaf9d80-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fd5013ea0c3f96931dec77174eaf9d80-Supplemental-Conference.pdf | We consider the problem of producing fair probabilistic classifiers for multi-class classification tasks. We formulate this problem in terms of ``projecting'' a pre-trained (and potentially unfair) classifier onto the set of models that satisfy target group-fairness requirements. The new, projected model is given by po... | null | null |
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness | https://papers.nips.cc/paper_files/paper/2022/hash/fd62b65606f0f0d2af2c01623a224258-Abstract-Conference.html | Mazda Moayeri, Kiarash Banihashem, Soheil Feizi | https://papers.nips.cc/paper_files/paper/2022/hash/fd62b65606f0f0d2af2c01623a224258-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17575-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fd62b65606f0f0d2af2c01623a224258-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fd62b65606f0f0d2af2c01623a224258-Supplemental-Conference.zip | Several existing works study either adversarial or natural distributional robustness of deep neural networks separately. In practice, however, models need to enjoy both types of robustness to ensure reliability. In this work, we bridge this gap and show that in fact, {\it explicit tradeoffs} exist between adversarial a... | null | null |
Learning Latent Seasonal-Trend Representations for Time Series Forecasting | https://papers.nips.cc/paper_files/paper/2022/hash/fd6613131889a4b656206c50a8bd7790-Abstract-Conference.html | Zhiyuan Wang, Xovee Xu, Weifeng Zhang, Goce Trajcevski, Ting Zhong, Fan Zhou | https://papers.nips.cc/paper_files/paper/2022/hash/fd6613131889a4b656206c50a8bd7790-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17680-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fd6613131889a4b656206c50a8bd7790-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fd6613131889a4b656206c50a8bd7790-Supplemental-Conference.pdf | Forecasting complex time series is ubiquitous and vital in a range of applications but challenging. Recent advances endeavor to achieve progress by incorporating various deep learning techniques (e.g., RNN and Transformer) into sequential models. However, clear patterns are still hard to extract since time series are o... | null | null |
Capturing Graphs with Hypo-Elliptic Diffusions | https://papers.nips.cc/paper_files/paper/2022/hash/fd7f43f8689988f4ef056f192ec0589b-Abstract-Conference.html | Csaba Toth, Darrick Lee, Celia Hacker, Harald Oberhauser | https://papers.nips.cc/paper_files/paper/2022/hash/fd7f43f8689988f4ef056f192ec0589b-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17484-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fd7f43f8689988f4ef056f192ec0589b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fd7f43f8689988f4ef056f192ec0589b-Supplemental-Conference.zip | Convolutional layers within graph neural networks operate by aggregating information about local neighbourhood structures; one common way to encode such substructures is through random walks. The distribution of these random walks evolves according to a diffusion equation defined using the graph Laplacian. We extend th... | null | null |
A Spectral Approach to Item Response Theory | https://papers.nips.cc/paper_files/paper/2022/hash/fd88ea50ca8c1973db037462f116ff99-Abstract-Conference.html | Duc Nguyen, Anderson Ye Zhang | https://papers.nips.cc/paper_files/paper/2022/hash/fd88ea50ca8c1973db037462f116ff99-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18395-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fd88ea50ca8c1973db037462f116ff99-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fd88ea50ca8c1973db037462f116ff99-Supplemental-Conference.zip | The Rasch model is one of the most fundamental models in item response theory and has wide-ranging applications from education testing to recommendation systems. In a universe with $n$ users and $m$ items, the Rasch model assumes that the binary response $X_{li} \in \{0,1\}$ of a user $l$ with parameter $\theta^*_l$ to... | null | null |
FedSR: A Simple and Effective Domain Generalization Method for Federated Learning | https://papers.nips.cc/paper_files/paper/2022/hash/fd946a6c99541fddc3d64a3ea39a1bc2-Abstract-Conference.html | A. Tuan Nguyen, Philip Torr, Ser Nam Lim | https://papers.nips.cc/paper_files/paper/2022/hash/fd946a6c99541fddc3d64a3ea39a1bc2-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16895-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fd946a6c99541fddc3d64a3ea39a1bc2-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fd946a6c99541fddc3d64a3ea39a1bc2-Supplemental-Conference.zip | Federated Learning (FL) refers to the decentralized and privacy-preserving machine learning framework in which multiple clients collaborate (with the help of a central server) to train a global model without sharing their data. However, most existing FL methods only focus on maximizing the model's performance on the so... | null | null |
SIXO: Smoothing Inference with Twisted Objectives | https://papers.nips.cc/paper_files/paper/2022/hash/fddc79681b2df2734c01444f9bc2a17e-Abstract-Conference.html | Dieterich Lawson, Allan Raventós, Andrew Warrington, Scott Linderman | https://papers.nips.cc/paper_files/paper/2022/hash/fddc79681b2df2734c01444f9bc2a17e-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18030-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fddc79681b2df2734c01444f9bc2a17e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fddc79681b2df2734c01444f9bc2a17e-Supplemental-Conference.pdf | Sequential Monte Carlo (SMC) is an inference algorithm for state space models that approximates the posterior by sampling from a sequence of target distributions. The target distributions are often chosen to be the filtering distributions, but these ignore information from future observations, leading to practical and ... | null | null |
Explicable Policy Search | https://papers.nips.cc/paper_files/paper/2022/hash/fdff3c4130c24c40c88aa41eb52d2a27-Abstract-Conference.html | Ze Gong, Yu ("Tony") Zhang | https://papers.nips.cc/paper_files/paper/2022/hash/fdff3c4130c24c40c88aa41eb52d2a27-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19116-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fdff3c4130c24c40c88aa41eb52d2a27-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fdff3c4130c24c40c88aa41eb52d2a27-Supplemental-Conference.pdf | Human teammates often form conscious and subconscious expectations of each other during interaction. Teaming success is contingent on whether such expectations can be met. Similarly, for an intelligent agent to operate beside a human, it must consider the human’s expectation of its behavior. Disregarding such expectati... | null | null |
Exploring evolution-aware & -free protein language models as protein function predictors | https://papers.nips.cc/paper_files/paper/2022/hash/fe066022bab2a6c6a3c57032a1623c70-Abstract-Conference.html | Mingyang Hu, Fajie Yuan, Kevin Yang, Fusong Ju, Jin Su, Hui Wang, Fei Yang, Qiuyang Ding | https://papers.nips.cc/paper_files/paper/2022/hash/fe066022bab2a6c6a3c57032a1623c70-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18083-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fe066022bab2a6c6a3c57032a1623c70-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fe066022bab2a6c6a3c57032a1623c70-Supplemental-Conference.zip | Large-scale Protein Language Models (PLMs) have improved performance in protein prediction tasks, ranging from 3D structure prediction to various function predictions. In particular, AlphaFold, a ground-breaking AI system, could potentially reshape structural biology. However, the utility of the PLM module in AlphaFold... | null | null |
Fair and Optimal Decision Trees: A Dynamic Programming Approach | https://papers.nips.cc/paper_files/paper/2022/hash/fe248e22b241ae5a9adf11493c8c12bc-Abstract-Conference.html | Jacobus van der Linden, Mathijs de Weerdt, Emir Demirović | https://papers.nips.cc/paper_files/paper/2022/hash/fe248e22b241ae5a9adf11493c8c12bc-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19373-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fe248e22b241ae5a9adf11493c8c12bc-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fe248e22b241ae5a9adf11493c8c12bc-Supplemental-Conference.pdf | Interpretable and fair machine learning models are required for many applications, such as credit assessment and in criminal justice. Decision trees offer this interpretability, especially when they are small. Optimal decision trees are of particular interest because they offer the best performance possible for a given... | null | null |
Recommender Forest for Efficient Retrieval | https://papers.nips.cc/paper_files/paper/2022/hash/fe2fe749d329627f161484876630c689-Abstract-Conference.html | Chao Feng, Wuchao Li, Defu Lian, Zheng Liu, Enhong Chen | https://papers.nips.cc/paper_files/paper/2022/hash/fe2fe749d329627f161484876630c689-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17503-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fe2fe749d329627f161484876630c689-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fe2fe749d329627f161484876630c689-Supplemental-Conference.pdf | Recommender systems (RS) have to select the top-N items from a massive item set. For the sake of efficient recommendation, RS usually represents user and item as latent embeddings, and relies on approximate nearest neighbour search (ANNs) to retrieve the recommendation result. Despite the reduction of running time, the... | null | null |
Graph Few-shot Learning with Task-specific Structures | https://papers.nips.cc/paper_files/paper/2022/hash/fe47dd3fd8e7eb43187d42d65083e383-Abstract-Conference.html | Song Wang, Chen Chen, Jundong Li | https://papers.nips.cc/paper_files/paper/2022/hash/fe47dd3fd8e7eb43187d42d65083e383-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18327-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fe47dd3fd8e7eb43187d42d65083e383-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fe47dd3fd8e7eb43187d42d65083e383-Supplemental-Conference.pdf | Graph few-shot learning is of great importance among various graph learning tasks. Under the few-shot scenario, models are often required to conduct classification given limited labeled samples. Existing graph few-shot learning methods typically leverage Graph Neural Networks (GNNs) and perform classification across a ... | null | null |
DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning | https://papers.nips.cc/paper_files/paper/2022/hash/fe51de4e7baf52e743b679e3bdba7905-Abstract-Conference.html | Quan Vuong, Aviral Kumar, Sergey Levine, Yevgen Chebotar | https://papers.nips.cc/paper_files/paper/2022/hash/fe51de4e7baf52e743b679e3bdba7905-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16888-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fe51de4e7baf52e743b679e3bdba7905-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fe51de4e7baf52e743b679e3bdba7905-Supplemental-Conference.pdf | In offline RL, constraining the learned policy to remain close to the data is essential to prevent the policy from outputting out-of-distribution (OOD) actions with erroneously overestimated values. In principle, generative adversarial networks (GAN) can provide an elegant solution to do so, with the discriminator dire... | null | null |
Beyond L1: Faster and Better Sparse Models with skglm | https://papers.nips.cc/paper_files/paper/2022/hash/fe5c31e525e9a26a1426ab0b589f42fe-Abstract-Conference.html | Quentin Bertrand, Quentin Klopfenstein, Pierre-Antoine Bannier, Gauthier Gidel, Mathurin Massias | https://papers.nips.cc/paper_files/paper/2022/hash/fe5c31e525e9a26a1426ab0b589f42fe-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18287-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fe5c31e525e9a26a1426ab0b589f42fe-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fe5c31e525e9a26a1426ab0b589f42fe-Supplemental-Conference.zip | We propose a new fast algorithm to estimate any sparse generalized linear model with convex or non-convex separable penalties. Our algorithm is able to solve problems with millions of samples and features in seconds, by relying on coordinate descent, working sets and Anderson acceleration. It handles previously unaddr... | null | null |
You Can’t Count on Luck: Why Decision Transformers and RvS Fail in Stochastic Environments | https://papers.nips.cc/paper_files/paper/2022/hash/fe90657b12193c7b52a3418bdc351807-Abstract-Conference.html | Keiran Paster, Sheila McIlraith, Jimmy Ba | https://papers.nips.cc/paper_files/paper/2022/hash/fe90657b12193c7b52a3418bdc351807-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18522-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fe90657b12193c7b52a3418bdc351807-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fe90657b12193c7b52a3418bdc351807-Supplemental-Conference.zip | Recently, methods such as Decision Transformer that reduce reinforcement learning to a prediction task and solve it via supervised learning (RvS) have become popular due to their simplicity, robustness to hyperparameters, and strong overall performance on offline RL tasks. However, simply conditioning a probabilistic m... | null | null |
Cost-efficient Gaussian tensor network embeddings for tensor-structured inputs | https://papers.nips.cc/paper_files/paper/2022/hash/fe91414cdc6348bcb5710e81bcb72c08-Abstract-Conference.html | Linjian Ma, Edgar Solomonik | https://papers.nips.cc/paper_files/paper/2022/hash/fe91414cdc6348bcb5710e81bcb72c08-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19342-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fe91414cdc6348bcb5710e81bcb72c08-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fe91414cdc6348bcb5710e81bcb72c08-Supplemental-Conference.pdf | This work discusses tensor network embeddings, which are random matrices ($S$) with tensor network structure. These embeddings have been used to perform dimensionality reduction of tensor network structured inputs $x$ and accelerate applications such as tensor decomposition and kernel regression. Existing works have de... | null | null |
Neural Transmitted Radiance Fields | https://papers.nips.cc/paper_files/paper/2022/hash/fe989bb038b5dcc44181255dd6913e43-Abstract-Conference.html | Chengxuan Zhu, Renjie Wan, Boxin Shi | https://papers.nips.cc/paper_files/paper/2022/hash/fe989bb038b5dcc44181255dd6913e43-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18353-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/fe989bb038b5dcc44181255dd6913e43-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/fe989bb038b5dcc44181255dd6913e43-Supplemental-Conference.pdf | Neural radiance fields (NeRF) have brought tremendous progress to novel view synthesis. Though NeRF enables the rendering of subtle details in a scene by learning from a dense set of images, it also reconstructs the undesired reflections when we capture images through glass. As a commonly observed interference, the ref... | null | null |
Unsupervised Skill Discovery via Recurrent Skill Training | https://papers.nips.cc/paper_files/paper/2022/hash/ff6b031d5bdc552b795175a0f3b35a50-Abstract-Conference.html | Zheyuan Jiang, Jingyue Gao, Jianyu Chen | https://papers.nips.cc/paper_files/paper/2022/hash/ff6b031d5bdc552b795175a0f3b35a50-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17057-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/ff6b031d5bdc552b795175a0f3b35a50-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/ff6b031d5bdc552b795175a0f3b35a50-Supplemental-Conference.zip | Being able to discover diverse useful skills without external reward functions is beneficial in reinforcement learning research. Previous unsupervised skill discovery approaches mainly train different skills in parallel. Although impressive results have been provided, we found that parallel training procedure can somet... | null | null |
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport | https://papers.nips.cc/paper_files/paper/2022/hash/ff7373914a96956f2a7cacbdf3b0b8d8-Abstract-Conference.html | Matthias Bitzer, Mona Meister, Christoph Zimmer | https://papers.nips.cc/paper_files/paper/2022/hash/ff7373914a96956f2a7cacbdf3b0b8d8-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18418-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/ff7373914a96956f2a7cacbdf3b0b8d8-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/ff7373914a96956f2a7cacbdf3b0b8d8-Supplemental-Conference.zip | Despite recent advances in automated machine learning, model selection is still a complex and computationally intensive process. For Gaussian processes (GPs), selecting the kernel is a crucial task, often done manually by the expert. Additionally, evaluating the model selection criteria for Gaussian processes typically... | null | null |
Robust Models are less Over-Confident | https://papers.nips.cc/paper_files/paper/2022/hash/ff887781480973bd3cb6026feb378d1e-Abstract-Conference.html | Julia Grabinski, Paul Gavrikov, Janis Keuper, Margret Keuper | https://papers.nips.cc/paper_files/paper/2022/hash/ff887781480973bd3cb6026feb378d1e-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18225-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/ff887781480973bd3cb6026feb378d1e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/ff887781480973bd3cb6026feb378d1e-Supplemental-Conference.pdf | Despite the success of convolutional neural networks (CNNs) in many academic benchmarks for computer vision tasks, their application in the real-world is still facing fundamental challenges. One of these open problems is the inherent lack of robustness, unveiled by the striking effectiveness of adversarial attacks. Cur... | null | null |
Near-Optimal No-Regret Learning Dynamics for General Convex Games | https://papers.nips.cc/paper_files/paper/2022/hash/ffa1301939cc707d6e986e6c4124340b-Abstract-Conference.html | Gabriele Farina, Ioannis Anagnostides, Haipeng Luo, Chung-Wei Lee, Christian Kroer, Tuomas Sandholm | https://papers.nips.cc/paper_files/paper/2022/hash/ffa1301939cc707d6e986e6c4124340b-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17332-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/ffa1301939cc707d6e986e6c4124340b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/ffa1301939cc707d6e986e6c4124340b-Supplemental-Conference.pdf | A recent line of work has established uncoupled learning dynamics such that, when employed by all players in a game, each player's regret after $T$ repetitions grows polylogarithmically in $T$, an exponential improvement over the traditional guarantees within the no-regret framework. However, so far these results... | null | null |
OTKGE: Multi-modal Knowledge Graph Embeddings via Optimal Transport | https://papers.nips.cc/paper_files/paper/2022/hash/ffdb280e7c7b4c4af30e04daf5a84b98-Abstract-Conference.html | Zongsheng Cao, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang | https://papers.nips.cc/paper_files/paper/2022/hash/ffdb280e7c7b4c4af30e04daf5a84b98-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17651-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/ffdb280e7c7b4c4af30e04daf5a84b98-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/ffdb280e7c7b4c4af30e04daf5a84b98-Supplemental-Conference.pdf | Multi-modal knowledge graph embeddings (KGE) have caught more and more attention in learning representations of entities and relations for link prediction tasks. Different from previous uni-modal KGE approaches, multi-modal KGE can leverage expressive knowledge from a wealth of modalities (image, text, etc.), leading t... | null | null |
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