Paper

  • On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks

    Jiong Zhu, Gaotang Li, Yao-An Yang, Jing Zhu, Xuehao Cui, Danai Koutra p65823-65851 from Advances in Neural Information Processing Systems 37
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  • On the Impacts of the Random Initialization in the Neural Tangent Kernel Theory

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Impacts of the Random Initialization in the Neural Tangent Kernel Theory

    Guhan Chen, Yicheng Li, Qian Lin p35909-35944 from Advances in Neural Information Processing Systems 37
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  • On the Implicit Bias of Linear Equivariant Steerable Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Implicit Bias of Linear Equivariant Steerable Networks

    Ziyu Chen, Wei Zhu p6132-6155 from Advances in Neural Information Processing Systems 36
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  • On the Importance of Exploration for Generalization in Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Importance of Exploration for Generalization in Reinforcement Learning

    Yiding Jiang, J. Zico Kolter, Roberta Raileanu p12951-12986 from Advances in Neural Information Processing Systems 36
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  • On the Importance of Feature Separability in Predicting Out-Of-Distribution Error

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Importance of Feature Separability in Predicting Out-Of-Distribution Error

    Renchunzi Xie, Hongxin Wei, Lei Feng, Yuzhou Cao, Bo An p27783-27800 from Advances in Neural Information Processing Systems 36
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  • On the Importance of Gradient Norm in PAC-Bayesian Bounds

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Importance of Gradient Norm in PAC-Bayesian Bounds

    Itai Gat, Yossi Adi, Alex Schwing, Tamir Hazan p16068-16081 from Advances in Neural Information Processing Systems 35
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  • On the Inability of Gaussian Process Regression to Optimally Learn Compositional Functions

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Inability of Gaussian Process Regression to Optimally Learn Compositional Functions

    Matteo Giordano, Kolyan Ray, Johannes Schmidt-Hieber p22341-22353 from Advances in Neural Information Processing Systems 35
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  • On the Inductive Bias of Stacking Towards Improving Reasoning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Inductive Bias of Stacking Towards Improving Reasoning

    Nikunj Saunshi, Stefani Karp, Shankar Krishnan, Sobhan Miryoosefi, Sashank Reddi, Sanjiv Kumar p71437-71464 from Advances in Neural Information Processing Systems 37
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  • On the Interplay between Social Welfare and Tractability of Equilibria

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Interplay between Social Welfare and Tractability of Equilibria

    Ioannis Anagnostides, Tuomas Sandholm p49927-49959 from Advances in Neural Information Processing Systems 36
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  • On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity

    Vincent Szolnoky, Viktor Andersson, Balazs Kulcsar, Rebecka Jörnsten p16319-16330 from Advances in Neural Information Processing Systems 35
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  • On the Last-iterate Convergence in Time-varying Zero-sum Games: Extra Gradient Succeeds where Optimism Fails

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Last-iterate Convergence in Time-varying Zero-sum Games: Extra Gradient Succeeds where Optimism Fails

    Yi Feng, Hu Fu, Qun Hu, Ping Li, Ioannis Panageas, Bo Peng, Xiao Wang p21933-21944 from Advances in Neural Information Processing Systems 36
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  • On the Learnability of Multilabel Ranking

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Learnability of Multilabel Ranking

    Vinod Raman, Unique Subedi, Ambuj Tewari p11988-11999 from Advances in Neural Information Processing Systems 36
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