Paper

  • Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance?

    Garud Iyengar, Henry Lam, Tianyu Wang p94736-94775 from Advances in Neural Information Processing Systems 37
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  • Is Distance Matrix Enough for Geometric Deep Learning?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Is Distance Matrix Enough for Geometric Deep Learning?

    Yinan Huang, Zian Li, Xiyuan Wang, Muhan Zhang p37413-37447 from Advances in Neural Information Processing Systems 36
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  • Is Function Similarity Over-Engineered? Building a Benchmark

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Is Function Similarity Over-Engineered? Building a Benchmark

    Noah Fleischmann, James Holt, Chang Liu, Kristopher Micinski, Edward Raff, Rebecca Saul, Richard Zak p21636-21655 from Advances in Neural Information Processing Systems 37
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  • Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning

    Chen Chen, Xin Dong, Guodong Long, Lingjuan Lyu, Yue Tan, Weiming Zhuang p27167-27180 from Advances in Neural Information Processing Systems 36
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  • Is Integer Arithmetic Enough for Deep Learning Training?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Is Integer Arithmetic Enough for Deep Learning Training?

    Masoud Asgharian, Alireza Ghaffari, Vahid Partovi Nia, Marzieh S. Tahaei, Mohammadreza Tayaranian p27402-27413 from Advances in Neural Information Processing Systems 35
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  • Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interactions

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interactions

    Nivasini Ananthakrishnan, Nika Haghtalab, Chara Podimata, Kunhe Yang p23852-23880 from Advances in Neural Information Processing Systems 37
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  • Is L2 Physics Informed Loss Always Suitable for Training Physics Informed Neural Network?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Is L2 Physics Informed Loss Always Suitable for Training Physics Informed Neural Network?

    Di He, Shanda Li, Liwei Wang, Chuwei Wang p8278-8290 from Advances in Neural Information Processing Systems 35
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  • Is Learning in Games Good for the Learners?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Is Learning in Games Good for the Learners?

    William Brown, Jon Schneider, Kiran Vodrahalli p54228-54249 from Advances in Neural Information Processing Systems 36
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  • Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?

    Yang Dai, Shengchao Hu, Jincai Huang, Shouling Ji, Xingxing Liang, Oubo Ma, Li Shen, Mengzhu Wang, Longfei Zhang p51474-51502 from Advances in Neural Information Processing Systems 37
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  • Is Multiple Object Tracking a Matter of Specialization?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Is Multiple Object Tracking a Matter of Specialization?

    Mattia Bernardi, Simone Calderara, Rita Cucchiara, Gianluca Mancusi, Aniello Panariello, Angelo Porrello p133776-133800 from Advances in Neural Information Processing Systems 37
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  • Is O(log N) practical? Near-Equivalence Between Delay Robustness and Bounded Regret in Bandits and RL

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Is O(log N) practical? Near-Equivalence Between Delay Robustness and Bounded Regret in Bandits and RL

    Enoch H. Kang, P. R. Kumar p100270-100290 from Advances in Neural Information Processing Systems 37
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  • Is One Annotation Enough? - A Data-Centric Image Classification Benchmark for Noisy and Ambiguous Label Estimation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Is One Annotation Enough? - A Data-Centric Image Classification Benchmark for Noisy and Ambiguous Label Estimation

    Sabine Dippel, Vasco Grossmann, Rainer Kiko, Reinhard Koch, Mariusz Oszust, Matti Pastell, Lars Schmarje, Jenny Stracke, Anna Valros, Nina Volkmann, Claudius Zelenka p33215-33232 from Advances in Neural Information Processing Systems 35
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