Neural Information Processing Systems Foundation, Inc. (NeurIPS)

  • ConceptMix: A Compositional Image Generation Benchmark with Controllable Difficulty

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    ConceptMix: A Compositional Image Generation Benchmark with Controllable Difficulty

    Sanjeev Arora, Yangsibo Huang, Olga Russakovsky, Xindi Wu, Dingli Yu p86004-86047 from Advances in Neural Information Processing Systems 37
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  • Conditional Controllable Image Fusion

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Conditional Controllable Image Fusion

    Bing Cao, Qinghua Hu, Qilong Wang, Xingxin Xu, Pengfei Zhu p120311-120335 from Advances in Neural Information Processing Systems 37
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  • Conditional Density Estimation with Histogram Trees

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Conditional Density Estimation with Histogram Trees

    Matthijs van Leeuwen, Lincen Yang p117315-117339 from Advances in Neural Information Processing Systems 37
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  • Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand

    Matt Jordan, Murat Kocaoglu, Md Musfiqur Rahman p77269-77315 from Advances in Neural Information Processing Systems 37
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  • Conditional Outcome Equivalence: A Quantile Alternative to CATE

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Conditional Outcome Equivalence: A Quantile Alternative to CATE

    Josh Givens, Song Liu, Henry Reeve, Katarzyna Reluga p102634-102671 from Advances in Neural Information Processing Systems 37
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  • Conditional Synthesis of 3D Molecules with Time Correction Sampler

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Conditional Synthesis of 3D Molecules with Time Correction Sampler

    Jaehyeong Jo, Hojung Jung, Bongsang Kim, Dongkyu Lee, Youngrok Park, Laura Schmid, Jinwoo Shin, Se-Young Yun p75914-75941 from Advances in Neural Information Processing Systems 37
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  • Conditioning non-linear and infinite-dimensional diffusion processes

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Conditioning non-linear and infinite-dimensional diffusion processes

    Elizabeth Baker, Christy Hipsley, Michael Severinsen, Stefan Sommer, Gefan Yang p10801-10826 from Advances in Neural Information Processing Systems 37
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  • CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting

    Jianrong Ding, Haiming Jin, Linghe Kong, Zhanyu Liu, Guanjie Zheng p128227-128259 from Advances in Neural Information Processing Systems 37
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  • Confidence Calibration of Classifiers with Many Classes

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Confidence Calibration of Classifiers with Many Classes

    Faouzi Adjed, Stéphane Herbin, Adrien Le Coz p77686-77725 from Advances in Neural Information Processing Systems 37
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  • Confidence Regulation Neurons in Language Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Confidence Regulation Neurons in Language Models

    Yonatan Belinkov, Wes Gurnee, Neel Nanda, Mrinmaya Sachan, Xingyi Song, Alessandro Stolfo, Ben Wu p125019-125049 from Advances in Neural Information Processing Systems 37
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  • Confident Natural Policy Gradient for Local Planning in $q_\pi$-realizable Constrained MDPs

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Confident Natural Policy Gradient for Local Planning in $q_\pi$-realizable Constrained MDPs

    Csaba Szepesvári, Tian Tian, Lin Yang p76139-76176 from Advances in Neural Information Processing Systems 37
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  • Conformal Alignment: Knowing When to Trust Foundation Models with Guarantees

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Conformal Alignment: Knowing When to Trust Foundation Models with Guarantees

    Yu Gui, Ying Jin, Zhimei Ren p73884-73919 from Advances in Neural Information Processing Systems 37
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  • Conformal Classification with Equalized Coverage for Adaptively Selected Groups

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Conformal Classification with Equalized Coverage for Adaptively Selected Groups

    Matteo Sesia, Yanfei Zhou p108760-108823 from Advances in Neural Information Processing Systems 37
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  • Conformal Inverse Optimization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Conformal Inverse Optimization

    Timothy Chan, Erick Delage, Bo Lin p63534-63564 from Advances in Neural Information Processing Systems 37
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  • Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration

    Taha Belkhouja, Janardhan Doppa, Subhankar Ghosh, Yuanjie Shi, Yan Yan p132133-132178 from Advances in Neural Information Processing Systems 37
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  • Conformalized Credal Set Predictors

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Conformalized Credal Set Predictors

    Eyke Hüllermeier, Alireza Javanmardi, David Stutz p116987-117014 from Advances in Neural Information Processing Systems 37
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  • Conformalized Multiple Testing after Data-dependent Selection

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Conformalized Multiple Testing after Data-dependent Selection

    Yuyang Huo, Liuhua Peng, Xiaoning Wang, Changliang Zou p58574-58609 from Advances in Neural Information Processing Systems 37
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  • Conformalized Time Series with Semantic Features

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Conformalized Time Series with Semantic Features

    Baiting Chen, Lu Cheng, Zhimei Ren p121449-121474 from Advances in Neural Information Processing Systems 37
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  • Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data

    Xiaohong Chen, Yongmei Liu, Canran Xiao p137495-137520 from Advances in Neural Information Processing Systems 37
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  • Conjugate Bayesian Two-step Change Point Detection for Hawkes Process

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Conjugate Bayesian Two-step Change Point Detection for Hawkes Process

    Xiaoling Lu, Zeyue Zhang, Feng Zhou p120013-120037 from Advances in Neural Information Processing Systems 37
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  • Conjugated Semantic Pool Improves OOD Detection with Pre-trained Vision-Language Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Conjugated Semantic Pool Improves OOD Detection with Pre-trained Vision-Language Models

    Mengyuan Chen, Junyu Gao, Changsheng Xu p82560-82593 from Advances in Neural Information Processing Systems 37
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  • ConMe: Rethinking Evaluation of Compositional Reasoning for Modern VLMs

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    ConMe: Rethinking Evaluation of Compositional Reasoning for Modern VLMs

    Assaf Arbelle, Victor Butoi, Trevor Darrell, Sivan Doveh, Rogerio Feris, Chuang Gan, Jacob Hansen, Roei Herzig, Irene Huang, Leonid Karlinsky, Hilde Kuehne, Wei Lin, M. Mirza, Aude Oliva p22927-22946 from Advances in Neural Information Processing Systems 37
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  • Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning

    Shentong Mo, Shengbang Tong p2348-2377 from Advances in Neural Information Processing Systems 37
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  • Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data

    Cem Anil, Jan Betley, Dami Choi, Owain Evans, Roger Grosse, Sam Marks, Johannes Treutlein p140667-140730 from Advances in Neural Information Processing Systems 37
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