Paper

  • GraB: Finding Provably Better Data Permutations than Random Reshuffling

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    GraB: Finding Provably Better Data Permutations than Random Reshuffling

    Yucheng Lu, Wentao Guo, Christopher De Sa p8969-8981 from Advances in Neural Information Processing Systems 35
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  • GRADES AND STRENGTH CHRACTERISTICS OF LOW-DENSITY HARDWOOD, YELLOW POPLAR

    World Conference on Timber Engineering 2025

    GRADES AND STRENGTH CHRACTERISTICS OF LOW-DENSITY HARDWOOD, YELLOW POPLAR

    Chul-Ki Kim, Da-Bin Song, Sang-Joon Lee, Keon-Ho Kim p2193-2198 from 14th World Conference on Timber Engineering 2025 (WCTE 2025)
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  • Gradient Cuff: Detecting Jailbreak Attacks on Large Language Models by Exploring Refusal Loss Landscapes

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Gradient Cuff: Detecting Jailbreak Attacks on Large Language Models by Exploring Refusal Loss Landscapes

    Xiaomeng Hu, Pin-Yu Chen, Tsung-Yi Ho p126265-126296 from Advances in Neural Information Processing Systems 37
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  • Gradient Descent is Optimal Under Lower Restricted Secant Inequality and Upper Error Bound

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Gradient Descent is Optimal Under Lower Restricted Secant Inequality and Upper Error Bound

    Charles Guille-Escuret, Adam Ibrahim, Baptiste Goujaud, Ioannis Mitliagkas p24893-24904 from Advances in Neural Information Processing Systems 35
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  • Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy

    Anastasiia Koloskova, Ryan McKenna, Zachary Charles, John Rush, H. Brendan McMahan p35761-35773 from Advances in Neural Information Processing Systems 36
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  • Gradient Descent: The Ultimate Optimizer

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Gradient Descent: The Ultimate Optimizer

    Kartik Chandra, Audrey Xie, Jonathan Ragan-Kelley, Erik Meijer p8214-8225 from Advances in Neural Information Processing Systems 35
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  • Gradient Estimation with Discrete Stein Operators

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Gradient Estimation with Discrete Stein Operators

    Jiaxin Shi, Yuhao Zhou, Jessica Hwang, Michalis Titsias, Lester Mackey p25829-25841 from Advances in Neural Information Processing Systems 35
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  • Gradient Flossing: Improving Gradient Descent through Dynamic Control of Jacobians

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Gradient Flossing: Improving Gradient Descent through Dynamic Control of Jacobians

    Rainer Engelken p10412-10439 from Advances in Neural Information Processing Systems 36
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  • Gradient Flow Dynamics of Shallow ReLU Networks for Square Loss and Orthogonal Inputs

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Gradient Flow Dynamics of Shallow ReLU Networks for Square Loss and Orthogonal Inputs

    Etienne Boursier, Loucas Pillaud-Vivien, Nicolas Flammarion p20105-20118 from Advances in Neural Information Processing Systems 35
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  • Gradient Guidance for Diffusion Models: An Optimization Perspective

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Gradient Guidance for Diffusion Models: An Optimization Perspective

    Yingqing Guo, Hui Yuan, Yukang Yang, Minshuo Chen, Mengdi Wang p90736-90770 from Advances in Neural Information Processing Systems 37
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  • Gradient Informed Proximal Policy Optimization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Gradient Informed Proximal Policy Optimization

    Sanghyun Son, Laura Zheng, Ryan Sullivan, Yi-Ling Qiao, Ming Lin p8788-8814 from Advances in Neural Information Processing Systems 36
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  • Gradient Methods for Online DR-Submodular Maximization with Stochastic Long-Term Constraints

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Gradient Methods for Online DR-Submodular Maximization with Stochastic Long-Term Constraints

    Guanyu Nie, Vaneet Aggarwal, Christopher Quinn p20510-20539 from Advances in Neural Information Processing Systems 37
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