Neural Information Processing Systems Foundation, Inc. (NeurIPS)

  • On student-teacher deviations in distillation: does it pay to disobey?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On student-teacher deviations in distillation: does it pay to disobey?

    Srinadh Bhojanapalli, Sanjiv Kumar, Aditya Menon, Hossein Mobahi, Vaishnavh Nagarajan p5961-6000 from Advances in Neural Information Processing Systems 36
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  • On the Ability of Developers' Training Data Preservation of Learnware

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Ability of Developers' Training Data Preservation of Learnware

    Hao-Yi Lei, Zhi-Hao Tan, Zhi-Hua Zhou p36471-36513 from Advances in Neural Information Processing Systems 37
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  • On the Ability of Graph Neural Networks to Model Interactions Between Vertices

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Ability of Graph Neural Networks to Model Interactions Between Vertices

    Nadav Cohen, Noam Razin, Tom Verbin p26501-26545 from Advances in Neural Information Processing Systems 36
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  • On the Adversarial Robustness of Benjamini Hochberg

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Adversarial Robustness of Benjamini Hochberg

    Louis Chen, Matan Seri, Roberto Szechtman p90965-90988 from Advances in Neural Information Processing Systems 37
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  • On the Adversarial Robustness of Mixture of Experts

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Adversarial Robustness of Mixture of Experts

    Pranjal Awasthi, Srinadh Bhojanapalli, Rodolphe Jenatton, Joan Puigcerver, Carlos Riquelme p9660-9671 from Advances in Neural Information Processing Systems 35
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  • On the Adversarial Robustness of Out-of-distribution Generalization Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Adversarial Robustness of Out-of-distribution Generalization Models

    Weiwei Liu, Xin Zou p68908-68938 from Advances in Neural Information Processing Systems 36
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  • On the Asymptotic Learning Curves of Kernel Ridge Regression under Power-law Decay

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Asymptotic Learning Curves of Kernel Ridge Regression under Power-law Decay

    Yicheng Li, Qian Lin, Haobo Zhang p49341-49364 from Advances in Neural Information Processing Systems 36
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  • On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift

    Amrith Setlur, Virginia Smith, Pratiksha Thaker, Zhiwei Wu p27088-27120 from Advances in Neural Information Processing Systems 37
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  • On the choice of Perception Loss Function for Learned Video Compression

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the choice of Perception Loss Function for Learned Video Compression

    Jun Chen, Ashish Khisti, Truong Buu Phan, Sadaf Salehkalaibar, Wei Yu p48226-48274 from Advances in Neural Information Processing Systems 36
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  • On the cohesion and separability of average-link for hierarchical agglomerative clustering

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the cohesion and separability of average-link for hierarchical agglomerative clustering

    Miguel Batista, Eduardo Laber p48710-48739 from Advances in Neural Information Processing Systems 37
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  • On the Comparison between Multi-modal and Single-modal Contrastive Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Comparison between Multi-modal and Single-modal Contrastive Learning

    Yuan Cao, Yongqiang Chen, Andi Han, Wei Huang, Taiji Suzuki, Zhiqiang Xu p81549-81605 from Advances in Neural Information Processing Systems 37
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  • On the Complexity of Adversarial Decision Making

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Complexity of Adversarial Decision Making

    Dylan J Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan p35404-35417 from Advances in Neural Information Processing Systems 35
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  • On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence

    Aymen Al Marjani, Achraf Azize, Debabrota Basu, Marc Jourdan p71150-71194 from Advances in Neural Information Processing Systems 36
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  • On the Complexity of Identification in Linear Structural Causal Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Complexity of Identification in Linear Structural Causal Models

    Markus Bläser, Julian Dörfler, Maciej Liśkiewicz, Benito van der Zander p100108-100130 from Advances in Neural Information Processing Systems 37
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  • On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries

    Nirmit Joshi, Theodor Misiakiewicz, Nathan Srebro p103198-103241 from Advances in Neural Information Processing Systems 37
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  • On the Complexity of Teaching a Family of Linear Behavior Cloning Learners

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Complexity of Teaching a Family of Linear Behavior Cloning Learners

    Shubham Bharti, Adish Singla, Stephen Wright, Jerry Zhu p103945-103965 from Advances in Neural Information Processing Systems 37
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  • On the Computational Complexity of Private High-dimensional Model Selection

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Computational Complexity of Private High-dimensional Model Selection

    Saptarshi Roy, Ambuj Tewari, Zehua Wang p36683-36716 from Advances in Neural Information Processing Systems 37
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  • On the Computational Landscape of Replicable Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Computational Landscape of Replicable Learning

    Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas, Felix Zhou p105887-105927 from Advances in Neural Information Processing Systems 37
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  • On the Connection between Pre-training Data Diversity and Fine-tuning Robustness

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Connection between Pre-training Data Diversity and Fine-tuning Robustness

    Ali Farhadi, Thao Nguyen, Sewoong Oh, Vivek Ramanujan, Ludwig Schmidt p66426-66437 from Advances in Neural Information Processing Systems 36
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  • On the Consistency of Maximum Likelihood Estimation of Probabilistic Principal Component Analysis

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Consistency of Maximum Likelihood Estimation of Probabilistic Principal Component Analysis

    Sayak Chakrabarty, Arghya Datta p28648-28662 from Advances in Neural Information Processing Systems 36
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  • On the Consistent Estimation of Optimal Receiver Operating Characteristic (ROC) Curve

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Consistent Estimation of Optimal Receiver Operating Characteristic (ROC) Curve

    Renxiong Liu, Yunzhang Zhu p30720-30731 from Advances in Neural Information Processing Systems 35
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  • On the Constrained Time-Series Generation Problem

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Constrained Time-Series Generation Problem

    Daniel Borrajo, Andrea Coletta, Sriram Gopalakrishnan, Svitlana Vyetrenko p61048-61059 from Advances in Neural Information Processing Systems 36
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  • On the Convergence and Sample Complexity Analysis of Deep Q-Networks with $\epsilon$-Greedy Exploration

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Convergence and Sample Complexity Analysis of Deep Q-Networks with $\epsilon$-Greedy Exploration

    Subhajit Chaudhury, Pin-Yu Chen, Hongkang Li, Miao Liu, Sijia Liu, Songtao Lu, Keerthiram Murugesan, Meng Wang, Shuai Zhang p13064-13102 from Advances in Neural Information Processing Systems 36
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  • On the Convergence of Black-Box Variational Inference

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    On the Convergence of Black-Box Variational Inference

    Jacob Gardner, Kyurae Kim, Yian Ma, Jisu Oh, Kaiwen Wu p44615-44657 from Advances in Neural Information Processing Systems 36
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