Paper

  • Learning from Distributed Users in Contextual Linear Bandits Without Sharing the Context

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Distributed Users in Contextual Linear Bandits Without Sharing the Context

    Christina Fragouli, Osama Hanna, Lin Yang p11049-11062 from Advances in Neural Information Processing Systems 35
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  • Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales

    P. R. Kumar, Tao Liu, Xi Liu, Ruida Zhou p9151-9163 from Advances in Neural Information Processing Systems 35
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  • Learning from Future: A Novel Self-Training Framework for Semantic Segmentation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Future: A Novel Self-Training Framework for Semantic Segmentation

    Ye Du, Jingjing Fei, Zehua Fu, Wei Li, Qingjie Liu, Yujun Shen, Haochen Wang, Liwei Wu, Rui Zhao p4749-4761 from Advances in Neural Information Processing Systems 35
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  • Learning from higher-order correlations, efficiently: hypothesis tests, random features, and neural networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from higher-order correlations, efficiently: hypothesis tests, random features, and neural networks

    Lorenzo Bardone, Federica Gerace, Sebastian Goldt, Eszter Székely p78479-78522 from Advances in Neural Information Processing Systems 37
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  • Learning from Highly Sparse Spatio-temporal Data

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Highly Sparse Spatio-temporal Data

    Enhong Chen, Leyan Deng, Defu Lian, Chenwang Wu p94022-94046 from Advances in Neural Information Processing Systems 37
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  • Learning from Label Proportions by Learning with Label Noise

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Label Proportions by Learning with Label Noise

    Clay Scott, Yutong Wang, Jianxin Zhang p26933-26942 from Advances in Neural Information Processing Systems 35
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  • Learning from Noisy Labels via Conditional Distributionally Robust Optimization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Noisy Labels via Conditional Distributionally Robust Optimization

    Hui Guo, Boyu Wang, Grace Yi p82627-82672 from Advances in Neural Information Processing Systems 37
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  • Learning from Offline Foundation Features with Tensor Augmentations

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Offline Foundation Features with Tensor Augmentations

    Emir Konuk, Phitchapha Lertsiravaramet, Christos Matsoukas, Kevin Smith, Moein Sorkhei p120103-120123 from Advances in Neural Information Processing Systems 37
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  • Learning from Pattern Completion: Self-supervised Controllable Generation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Pattern Completion: Self-supervised Controllable Generation

    Zhiqiang Chen, Guofan Fan, Jinying Gao, Tiejun Huang, Bo Lei, Lei Ma, Shan Yu p27207-27235 from Advances in Neural Information Processing Systems 37
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  • Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection

    Dongdong Chen, Yi-Ling Chen, Yinpeng Chen, Xiyang Dai, Yu-Gang Jiang, Mengchen Liu, Lingchen Meng, Zuxuan Wu, Jianwei Yang, Lu Yuan p78082-78094 from Advances in Neural Information Processing Systems 36
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  • Learning from Snapshots of Discrete and Continuous Data Streams

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Snapshots of Discrete and Continuous Data Streams

    Pramith Devulapalli, Steve Hanneke p80082-80106 from Advances in Neural Information Processing Systems 37
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  • Learning from Stochastically Revealed Preference

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Stochastically Revealed Preference

    John Birge, Xiaocheng Li, Chunlin Sun p35061-35071 from Advances in Neural Information Processing Systems 35
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