MetricOpt: Learning to Optimize Black-Box Evaluation Metrics
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Shuangfei Zhai | Pengsheng Guo | Josh Susskind | Chen Huang | J. Susskind | Shuangfei Zhai | Chen Huang | Pengsheng Guo
[1] P. Pérez,et al. SoDeep: A Sorting Deep Net to Learn Ranking Loss Surrogates , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Silvio Savarese,et al. Deep Metric Learning via Lifted Structured Feature Embedding , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Marcin Andrychowicz,et al. Learning to learn by gradient descent by gradient descent , 2016, NIPS.
[4] Misha Denil,et al. Learned Optimizers that Scale and Generalize , 2017, ICML.
[5] Lijun Wu,et al. Learning to Teach with Dynamic Loss Functions , 2018, NeurIPS.
[6] Stan Sclaroff,et al. Deep Metric Learning to Rank , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Kun He,et al. Hashing as Tie-Aware Learning to Rank , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[8] Jascha Sohl-Dickstein,et al. Guided evolutionary strategies: augmenting random search with surrogate gradients , 2018, ICML.
[9] Hugo Larochelle,et al. Modulating early visual processing by language , 2017, NIPS.
[10] Herke van Hoof,et al. Addressing Function Approximation Error in Actor-Critic Methods , 2018, ICML.
[11] Harikrishna Narasimhan,et al. On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures , 2014, NIPS.
[12] Jia Deng,et al. A Unified Framework of Surrogate Loss by Refactoring and Interpolation , 2020, ECCV.
[13] Carlos Guestrin,et al. Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment , 2019, ICML.
[14] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.
[15] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[16] Christopher K. I. Williams,et al. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .
[17] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[18] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[19] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[20] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Yan Lu,et al. Local Descriptors Optimized for Average Precision , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[22] Qijia Jiang,et al. Optimizing Black-box Metrics with Adaptive Surrogates , 2020, ICML.
[23] Eric P. Xing,et al. AutoLoss: Learning Discrete Schedules for Alternate Optimization , 2018, ICLR 2018.
[24] Quoc V. Le,et al. Neural Optimizer Search with Reinforcement Learning , 2017, ICML.
[25] Oluwasanmi Koyejo,et al. Consistent Binary Classification with Generalized Performance Metrics , 2014, NIPS.
[26] Jiri Matas,et al. Learning Surrogates via Deep Embedding , 2020, ECCV.
[27] Vijay Vasudevan,et al. Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[28] Tamir Hazan,et al. Direct Loss Minimization for Structured Prediction , 2010, NIPS.
[29] Claudio Michaelis,et al. Optimizing Rank-Based Metrics With Blackbox Differentiation , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Joshua Achiam,et al. On First-Order Meta-Learning Algorithms , 2018, ArXiv.
[31] Razvan Pascanu,et al. Meta-Learning with Warped Gradient Descent , 2020, ICLR.
[32] Yves Grandvalet,et al. Optimizing F-Measures by Cost-Sensitive Classification , 2014, NIPS.
[33] C. V. Jawahar,et al. Efficient Optimization for Rank-Based Loss Functions , 2016, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[34] Aaron C. Courville,et al. FiLM: Visual Reasoning with a General Conditioning Layer , 2017, AAAI.
[35] Vittorio Ferrari,et al. End-to-End Training of Object Class Detectors for Mean Average Precision , 2016, ACCV.
[36] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[37] Jeremy Nixon,et al. Understanding and correcting pathologies in the training of learned optimizers , 2018, ICML.
[38] Katja Hofmann,et al. Fast Context Adaptation via Meta-Learning , 2018, ICML.
[39] Sergey Levine,et al. Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor , 2018, ICML.
[40] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[41] Meng Yang,et al. Large-Margin Softmax Loss for Convolutional Neural Networks , 2016, ICML.
[42] Matthew B. Blaschko,et al. The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[43] Ling-Yu Duan,et al. Towards Accurate One-Stage Object Detection With AP-Loss , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[44] Yang Song,et al. Training Deep Neural Networks via Direct Loss Minimization , 2015, ICML.
[45] Matthew R. Scott,et al. Multi-Similarity Loss With General Pair Weighting for Deep Metric Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[46] Alain Rakotomamonjy,et al. Optimizing Area Under Roc Curve with SVMs , 2004, ROCAI.
[47] Mahdi Milani Fard,et al. Metric-Optimized Example Weights , 2018, ICML.
[48] Hao Li,et al. Visualizing the Loss Landscape of Neural Nets , 2017, NeurIPS.
[49] Fred A. Hamprecht,et al. Essentially No Barriers in Neural Network Energy Landscape , 2018, ICML.
[50] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.