Neural Information Processing Systems Foundation, Inc. (NeurIPS)

  • Improving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual Recognition

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Improving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual Recognition

    Yiu-ming Cheung, Hui Huang, Mengke Li, Ye Liu, Yang Lu, Yiqun Zhang p103985-104009 from Advances in Neural Information Processing Systems 37
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  • Improving Zero-Shot Generalization in Offline Reinforcement Learning Using Generalized Similarity Functions

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Improving Zero-Shot Generalization in Offline Reinforcement Learning Using Generalized Similarity Functions

    Ilya Kostrikov, Bogdan Mazoure, Ofir Nachum, Jonathan Tompson p25088-25101 from Advances in Neural Information Processing Systems 35
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  • In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defer

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defer

    Bo An, Yuzhou Cao, Lei Feng, Hussein Mozannar, Hongxin Wei p38485-38503 from Advances in Neural Information Processing Systems 36
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  • In Defense of the Unitary Scalarization for Deep Multi-Task Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In Defense of the Unitary Scalarization for Deep Multi-Task Learning

    Alessandro De Palma, Ilya Kostrikov, Vitaly Kurin, Pawan K Mudigonda, Shimon Whiteson p12169-12183 from Advances in Neural Information Processing Systems 35
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  • In Differential Privacy, There is Truth: On Vote-Histogram Leakage in Ensemble Private Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In Differential Privacy, There is Truth: On Vote-Histogram Leakage in Ensemble Private Learning

    David Lie, Nicolas Papernot, Roei Schuster, I Shumailov, Jiaqi Wang p29026-29037 from Advances in Neural Information Processing Systems 35
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  • In Pursuit of Causal Label Correlations for Multi-label Image Recognition

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In Pursuit of Causal Label Correlations for Multi-label Image Recognition

    Sixian Chan, Zhao-Min Chen, Yisu Ge, Xin Jin p51634-51654 from Advances in Neural Information Processing Systems 37
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  • In the Eye of the Beholder: Robust Prediction with Causal User Modeling

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In the Eye of the Beholder: Robust Prediction with Causal User Modeling

    Amir Feder, Guy Horowitz, Roi Reichart, Nir Rosenfeld, Yoav Wald p14419-14433 from Advances in Neural Information Processing Systems 35
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  • In What Ways Are Deep Neural Networks Invariant and How Should We Measure This?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In What Ways Are Deep Neural Networks Invariant and How Should We Measure This?

    Tim Doster, Tegan Emerson, Grayson Jorgenson, Henry Kvinge, Jesse Lew, Scott Vasquez p32816-32829 from Advances in Neural Information Processing Systems 35
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  • In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies

    Yunbum Kook, Santosh Vempala, Matthew Zhang p108354-108388 from Advances in Neural Information Processing Systems 37
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  • In-Context Impersonation Reveals Large Language Models' Strengths and Biases

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-Context Impersonation Reveals Large Language Models' Strengths and Biases

    Zeynep Akata, Stephan Alaniz, Isabel Rio-Torto, Leonard Salewski, Eric Schulz p72044-72057 from Advances in Neural Information Processing Systems 36
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  • In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization

    Peter Bartlett, Jingfeng Wu, Ruiqi Zhang p18310-18361 from Advances in Neural Information Processing Systems 37
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  • In-Context Learning State Vector with Inner and Momentum Optimization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-Context Learning State Vector with Inner and Momentum Optimization

    Baotian Hu, Xinshuo Hu, Dongfang Li, Zhenyu Liu, Zetian Sun, Min Zhang p7797-7820 from Advances in Neural Information Processing Systems 37
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  • In-Context Learning Unlocked for Diffusion Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-Context Learning Unlocked for Diffusion Models

    Weizhu Chen, Pengcheng He, Yifan Jiang, Yadong Lu, Yelong Shen, Zhangyang Atlas Wang, Zhendong Wang, Mingyuan Zhou p8542-8562 from Advances in Neural Information Processing Systems 36
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  • In-Context Learning with Representations: Contextual Generalization of Trained Transformers

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-Context Learning with Representations: Contextual Generalization of Trained Transformers

    Yuejie Chi, Yu Huang, Yingbin Liang, Tong Yang p85867-85898 from Advances in Neural Information Processing Systems 37
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  • In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness

    Liam Collins, Aryan Mokhtari, Advait Parulekar, Sujay Sanghavi, Sanjay Shakkottai p92638-92696 from Advances in Neural Information Processing Systems 37
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  • In-Context Symmetries: Self-Supervised Learning through Contextual World Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-Context Symmetries: Self-Supervised Learning through Contextual World Models

    Sharut Gupta, Tommi Jaakkola, Stefanie Jegelka, Chenyu Wang, Yifei Wang p104250-104280 from Advances in Neural Information Processing Systems 37
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  • In-N-Out: Lifting 2D Diffusion Prior for 3D Object Removal via Tuning-Free Latents Alignment

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-N-Out: Lifting 2D Diffusion Prior for 3D Object Removal via Tuning-Free Latents Alignment

    Tingjin Chu, Huan Fu, Mingming Gong, Jiaxian Guo, Dongting Hu, Feng Liu, Tongliang Liu, Liuhua Peng p45737-45766 from Advances in Neural Information Processing Systems 37
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  • In-Trajectory Inverse Reinforcement Learning: Learn Incrementally Before an Ongoing Trajectory Terminates

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-Trajectory Inverse Reinforcement Learning: Learn Incrementally Before an Ongoing Trajectory Terminates

    Shicheng Liu, Minghui Zhu p117164-117209 from Advances in Neural Information Processing Systems 37
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  • Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization

    Bo Li, Ruta Mehta, Aniket Murhekar, Bhaskar Ray Chaudhury, Zhuowen Yuan p17811-17831 from Advances in Neural Information Processing Systems 36
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  • Incentives in Private Collaborative Machine Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Incentives in Private Collaborative Machine Learning

    Nghia Hoang, Patrick Jaillet, Bryan Kian Hsiang Low, Rachael Sim, Xinyi Xu, Yehong Zhang p7555-7593 from Advances in Neural Information Processing Systems 36
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  • Incentivized Communication for Federated Bandits

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Incentivized Communication for Federated Bandits

    Chuanhao Li, Hongning Wang, Zhepei Wei, Haifeng Xu p54399-54420 from Advances in Neural Information Processing Systems 36
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  • Incentivizing Combinatorial Bandit Exploration

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Incentivizing Combinatorial Bandit Exploration

    Xinyan Hu, Dung Ngo, Aleksandrs Slivkins, Steven Wu p37173-37183 from Advances in Neural Information Processing Systems 35
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  • Incentivizing Honesty among Competitors in Collaborative Learning and Optimization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Incentivizing Honesty among Competitors in Collaborative Learning and Optimization

    Florian E. Dorner, Nikola Konstantinov, Georgi Pashaliev, Martin Vechev p7659-7696 from Advances in Neural Information Processing Systems 36
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  • Incentivizing Quality Text Generation via Statistical Contracts

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Incentivizing Quality Text Generation via Statistical Contracts

    Ohad Einav, Eden Saig, Inbal Talgam-Cohen p51196-51222 from Advances in Neural Information Processing Systems 37
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