Neural Information Processing Systems Foundation, Inc. (NeurIPS)

  • A Swiss Army Knife for Heterogeneous Federated Learning: Flexible Coupling via Trace Norm

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Swiss Army Knife for Heterogeneous Federated Learning: Flexible Coupling via Trace Norm

    Chuan Chen, Jialong Chen, Lele Fu, Tianchi Liao, Zhen Wang, Zibin Zheng p139886-139911 from Advances in Neural Information Processing Systems 37
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  • A Synthetic Dataset for Personal Attribute Inference

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Synthetic Dataset for Personal Attribute Inference

    Robin Staab, Martin Vechev, Mark Vero, Hanna Yukhymenko p120735-120779 from Advances in Neural Information Processing Systems 37
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  • A Systematic Review of NeurIPS Dataset Management Practices

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Systematic Review of NeurIPS Dataset Management Practices

    Leah Ajmani, Hanlin Li, Shayne Longpre, Yiwei Wu p32813-32827 from Advances in Neural Information Processing Systems 37
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  • A Taxonomy of Challenges to Curating Fair Datasets

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Taxonomy of Challenges to Curating Fair Datasets

    Jerone Andrews, Pooja Chitre, Georgia Panagiotidou, Kathleen Pine, Morgan Scheuerman, Shawn Walker, Alice Xiang, Dora Zhao p97826-97858 from Advances in Neural Information Processing Systems 37
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  • A teacher-teacher framework for clinical language representation learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A teacher-teacher framework for clinical language representation learning

    Tianxi Cai, Feiqing Huang, Sara Sweet, Shenghan Zhang p115940-115962 from Advances in Neural Information Processing Systems 37
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  • A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis

    Chris Callison-Burch, Mona Gandhi, James Gee, Yufei Wang, Yifan Wu, Yue Yang, Michael Yao, Mark Yatskar p90683-90713 from Advances in Neural Information Processing Systems 37
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  • A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning

    Zachary Lipton, Tom Yan p27295-27339 from Advances in Neural Information Processing Systems 37
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  • A theoretical design of concept sets: improving the predictability of concept bottleneck models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A theoretical design of concept sets: improving the predictability of concept bottleneck models

    Max Ruiz Luyten, Mihaela van der Schaar p100160-100195 from Advances in Neural Information Processing Systems 37
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  • A Theoretical Perspective for Speculative Decoding Algorithm

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Theoretical Perspective for Speculative Decoding Algorithm

    Minshuo Chen, Kaixuan Huang, Mengdi Wang, Ming Yin p128082-128117 from Advances in Neural Information Processing Systems 37
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  • A Theoretical Understanding of Self-Correction through In-context Alignment

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Theoretical Understanding of Self-Correction through In-context Alignment

    Yisen Wang, Stefanie Jegelka, Yifei Wang, Zeming Wei, Yuyang Wu p89869-89912 from Advances in Neural Information Processing Systems 37
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  • A Theory of Optimistically Universal Online Learnability for General Concept Classes

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Theory of Optimistically Universal Online Learnability for General Concept Classes

    Steve Hanneke, Hongao Wang p52749-52775 from Advances in Neural Information Processing Systems 37
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  • A Topology-aware Graph Coarsening Framework for Continual Graph Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Topology-aware Graph Coarsening Framework for Continual Graph Learning

    Zhuo Feng, Xiaoxue Han, Yue Ning p132491-132523 from Advances in Neural Information Processing Systems 37
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  • A Tractable Inference Perspective of Offline RL

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Tractable Inference Perspective of Offline RL

    Yitao Liang, Anji Liu, Xuejie Liu, Guy Van den Broeck p70953-70980 from Advances in Neural Information Processing Systems 37
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  • A two-scale Complexity Measure for Deep Learning Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A two-scale Complexity Measure for Deep Learning Models

    Massimiliano Datres, Alessio Figalli, Gian Leonardi, David Sutter p23013-23039 from Advances in Neural Information Processing Systems 37
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  • A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits

    Kwang-Sung Jun, Jungyhun Lee, Se-Young Yun p124640-124685 from Advances in Neural Information Processing Systems 37
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  • A Unified Debiasing Approach for Vision-Language Models across Modalities and Tasks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Unified Debiasing Approach for Vision-Language Models across Modalities and Tasks

    Taeuk Jang, Hoin Jung, Xiaoqian Wang p21034-21058 from Advances in Neural Information Processing Systems 37
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  • A Unified Framework for 3D Scene Understanding

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Unified Framework for 3D Scene Understanding

    Xiang Bai, Dingkang Liang, Chunsheng Shi, Sifan Tu, Wei Xu, Xin Zhou p59468-59490 from Advances in Neural Information Processing Systems 37
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  • A Unified Principle of Pessimism for Offline Reinforcement Learning under Model Mismatch

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Unified Principle of Pessimism for Offline Reinforcement Learning under Model Mismatch

    Zhongchang Sun, Yue Wang, Shaofeng Zou p9281-9328 from Advances in Neural Information Processing Systems 37
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  • A Unifying Normative Framework of Decision Confidence

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Unifying Normative Framework of Decision Confidence

    Micheal Buice, Amelia Johnson, Koosha Khalvati p140847-140864 from Advances in Neural Information Processing Systems 37
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  • A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems

    Mohammad-Amin Charusaie, Samira Samadi p23705-23755 from Advances in Neural Information Processing Systems 37
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  • A Universal Growth Rate for Learning with Smooth Surrogate Losses

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Universal Growth Rate for Learning with Smooth Surrogate Losses

    Anqi Mao, Mehryar Mohri, Yutao Zhong p41670-41708 from Advances in Neural Information Processing Systems 37
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  • A Versatile Diffusion Transformer with Mixture of Noise Levels for Audiovisual Generation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Versatile Diffusion Transformer with Mixture of Noise Levels for Audiovisual Generation

    Agrim Gupta, Aren Jansen, Lu Jiang, Brendan Jou, Gwanghyun Kim, José Lezama, Alonso Martinez, Krishna Somandepalli, Yu-Chuan Su, Jacob Walker, Lijun Yu p11837-11865 from Advances in Neural Information Processing Systems 37
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  • A versatile informative diffusion model for single-cell ATAC-seq data generation and analysis

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A versatile informative diffusion model for single-cell ATAC-seq data generation and analysis

    Lei Huang, Manolis Kellis, Zunpeng Liu, Na Sun, Ka-Chun Wong, Lei Xiong p45462-45484 from Advances in Neural Information Processing Systems 37
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  • A Walsh Hadamard Derived Linear Vector Symbolic Architecture

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Walsh Hadamard Derived Linear Vector Symbolic Architecture

    Mohammad Mahmudul Alam, Stella Biderman, James Holt, Tim Oates, Alexander Oberle, Edward Raff p2711-2733 from Advances in Neural Information Processing Systems 37
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