Neural Information Processing Systems Foundation, Inc. (NeurIPS)

  • On the Sparsity of the Strong Lottery Ticket Hypothesis

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Sparsity of the Strong Lottery Ticket Hypothesis

    Davide Ferré, Giordano Giambartolomei, Frédéric Giroire, Frederik Mallmann-Trenn, Emanuele Natale p40565-40592 from Advances in Neural Information Processing Systems 37
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  • On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels

    Meirav Galun, Amnon Geifman, David Jacobs, Basri Ronen p11253-11265 from Advances in Neural Information Processing Systems 35
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  • On the spectral bias of two-layer linear networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the spectral bias of two-layer linear networks

    Nicolas Flammarion, Loucas Pillaud-Vivien, Aditya Vardhan Varre, Maria-Luiza Vladarean p64380-64414 from Advances in Neural Information Processing Systems 36
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  • On the Stability and Generalization of Meta-Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Stability and Generalization of Meta-Learning

    Raman Arora, Yunjuan Wang p83665-83710 from Advances in Neural Information Processing Systems 37
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  • On the Stability and Scalability of Node Perturbation Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Stability and Scalability of Node Perturbation Learning

    Naoki Hiratani, Peter E Latham, Timothy Lillicrap, Yash Mehta p31929-31941 from Advances in Neural Information Processing Systems 35
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  • On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm

    Qi Chen, Ligong Han, Mario Marchand, Changjian Shui p27414-27468 from Advances in Neural Information Processing Systems 36
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  • On the Statistical Consistency of Risk-Sensitive Bayesian Decision-Making

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Statistical Consistency of Risk-Sensitive Bayesian Decision-Making

    Harsha Honnappa, Prateek Jaiswal, Vinayak Rao p53158-53200 from Advances in Neural Information Processing Systems 36
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  • On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL

    Alekh Agarwal, Jinglin Chen, Nan Jiang, Akshay Krishnamurthy, Aditya Modi p20960-20973 from Advances in Neural Information Processing Systems 35
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  • On the Strong Correlation Between Model Invariance and Generalization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Strong Correlation Between Model Invariance and Generalization

    Weijian Deng, Stephen Gould, Liang Zheng p28052-28067 from Advances in Neural Information Processing Systems 35
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  • On the Sublinear Regret of GP-UCB

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Sublinear Regret of GP-UCB

    Aaditya Ramdas, Justin Whitehouse, Steven Wu p35266-35276 from Advances in Neural Information Processing Systems 36
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  • On the Surprising Effectiveness of Attention Transfer for Vision Transformers

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Surprising Effectiveness of Attention Transfer for Vision Transformers

    Beidi Chen, Xinlei Chen, Alexander Li, Deepak Pathak, Yuandong Tian p113963-113990 from Advances in Neural Information Processing Systems 37
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  • On the Symmetries of Deep Learning Models and Their Internal Representations

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Symmetries of Deep Learning Models and Their Internal Representations

    Davis Brown, Tegan Emerson, Charles Godfrey, Henry Kvinge p11893-11905 from Advances in Neural Information Processing Systems 35
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  • On the Symmetries of the Synchronization Problem in Cryo-EM: Multi-Frequency Vector Diffusion Maps on the Projective Plane

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Symmetries of the Synchronization Problem in Cryo-EM: Multi-Frequency Vector Diffusion Maps on the Projective Plane

    Arash Behboodi, Gabriele Cesa, Taco Cohen, Max Welling p5446-5458 from Advances in Neural Information Processing Systems 35
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  • On the Target-kernel Alignment: a Unified Analysis with Kernel Complexity

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Target-kernel Alignment: a Unified Analysis with Kernel Complexity

    Xin He, Chao Wang, Junhui Wang, Yuwen Wang p40434-40485 from Advances in Neural Information Processing Systems 37
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  • On the Theoretical Properties of Noise Correlation in Stochastic Optimization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Theoretical Properties of Noise Correlation in Stochastic Optimization

    Francis Bach, Hans Kersting, Aurelien Lucchi, Antonio Orvieto, Frank Proske p14261-14273 from Advances in Neural Information Processing Systems 35
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  • On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training

    Zhengyu Hu, Pang Wei Koh, Alexander Ratner, Bohan Wang, Jieyu Zhang p64193-64212 from Advances in Neural Information Processing Systems 36
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  • On the Tradeoff Between Robustness and Fairness

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Tradeoff Between Robustness and Fairness

    Weiwei Liu, Xinsong Ma, Zekai Wang p26230-26241 from Advances in Neural Information Processing Systems 35
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  • On the Use of Anchoring for Training Vision Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Use of Anchoring for Training Vision Models

    Rushil Anirudh, Yamen Mubarka, Vivek Narayanaswamy, Wesam Sakla, Jayaraman Thiagarajan, Kowshik Thopalli p95438-95455 from Advances in Neural Information Processing Systems 37
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  • On the Variance, Admissibility, and Stability of Empirical Risk Minimization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Variance, Admissibility, and Stability of Empirical Risk Minimization

    Gil Kur, Eli Putterman, Alexander Rakhlin p37527-37539 from Advances in Neural Information Processing Systems 36
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  • On the Worst Prompt Performance of Large Language Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Worst Prompt Performance of Large Language Models

    Deng Cai, Bowen Cao, Wai Lam, Zhisong Zhang, Yuexian Zou p69022-69042 from Advances in Neural Information Processing Systems 37
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  • On Tractable $\Phi$-Equilibria in Non-Concave Games

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On Tractable $\Phi$-Equilibria in Non-Concave Games

    Yang Cai, Constantinos Daskalakis, Haipeng Luo, Chen-Yu Wei, Weiqiang Zheng p140366-140404 from Advances in Neural Information Processing Systems 37
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  • On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks

    Maurice André Georgi, Laura F. Nern, Harsh Raj, Yash Sharma p59206-59226 from Advances in Neural Information Processing Systems 36
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  • On Translation and Reconstruction Guarantees of the Cycle-Consistent Generative Adversarial Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On Translation and Reconstruction Guarantees of the Cycle-Consistent Generative Adversarial Networks

    Anish Chakrabarty, Swagatam Das p23607-23620 from Advances in Neural Information Processing Systems 35
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  • On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification

    Pavel Izmailov, Sanyam Kapoor, Wesley Maddox, Andrew Wilson p18211-18225 from Advances in Neural Information Processing Systems 35
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