暂无分享,去创建一个
Sham M. Kakade | Wei Hu | Jason D. Lee | Qi Lei | Simon S. Du | S. Kakade | S. Du | J. Lee | Wei Hu | Qi Lei
[1] Sebastian Thrun,et al. Learning to Learn: Introduction and Overview , 1998, Learning to Learn.
[2] Rich Caruana,et al. Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.
[3] Jonathan Baxter,et al. A Model of Inductive Bias Learning , 2000, J. Artif. Intell. Res..
[4] Nicolas Le Roux,et al. Convex Neural Networks , 2005, NIPS.
[5] Adi Shraibman,et al. Rank, Trace-Norm and Max-Norm , 2005, COLT.
[6] Ji Zhu,et al. l1 Regularization in Infinite Dimensional Feature Spaces , 2007, COLT.
[7] Martin J. Wainwright,et al. Estimation of (near) low-rank matrices with noise and high-dimensional scaling , 2009, ICML.
[8] Sham M. Kakade,et al. A tail inequality for quadratic forms of subgaussian random vectors , 2011, ArXiv.
[9] Sham M. Kakade,et al. Random Design Analysis of Ridge Regression , 2012, COLT.
[10] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[11] René Vidal,et al. Structured Low-Rank Matrix Factorization: Optimality, Algorithm, and Applications to Image Processing , 2014, ICML.
[12] Joel A. Tropp,et al. An Introduction to Matrix Concentration Inequalities , 2015, Found. Trends Mach. Learn..
[13] Furong Huang,et al. Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition , 2015, COLT.
[14] Lior Wolf,et al. A theoretical framework for deep transfer learning , 2016 .
[15] Michael I. Jordan,et al. Gradient Descent Only Converges to Minimizers , 2016, COLT.
[16] Massimiliano Pontil,et al. The Benefit of Multitask Representation Learning , 2015, J. Mach. Learn. Res..
[17] Maria-Florina Balcan,et al. Risk Bounds for Transferring Representations With and Without Fine-Tuning , 2017, ICML.
[18] Andrew Zisserman,et al. Multi-task Self-Supervised Visual Learning , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[19] Pierre Alquier,et al. Regret Bounds for Lifelong Learning , 2016, AISTATS.
[20] Chen Sun,et al. Revisiting Unreasonable Effectiveness of Data in Deep Learning Era , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[21] Michael I. Jordan,et al. How to Escape Saddle Points Efficiently , 2017, ICML.
[22] Massimiliano Pontil,et al. Incremental Learning-to-Learn with Statistical Guarantees , 2018, UAI.
[23] Roman Vershynin,et al. Four lectures on probabilistic methods for data science , 2016, IAS/Park City Mathematics Series.
[24] Abhinav Gupta,et al. Scaling and Benchmarking Self-Supervised Visual Representation Learning , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[25] Luca Bertinetto,et al. Meta-learning with differentiable closed-form solvers , 2018, ICLR.
[26] Massimiliano Pontil,et al. Learning-to-Learn Stochastic Gradient Descent with Biased Regularization , 2019, ICML.
[27] Mikhail Khodak,et al. A Theoretical Analysis of Contrastive Unsupervised Representation Learning , 2019, ICML.
[28] Sergey Levine,et al. Meta-Learning , 2019, Automated Machine Learning.
[29] Colin Wei,et al. Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel , 2018, NeurIPS.
[30] Maria-Florina Balcan,et al. Adaptive Gradient-Based Meta-Learning Methods , 2019, NeurIPS.
[31] Subhransu Maji,et al. Meta-Learning With Differentiable Convex Optimization , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Michael I. Jordan,et al. On the Theory of Transfer Learning: The Importance of Task Diversity , 2020, NeurIPS.
[33] Ruosong Wang,et al. Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks , 2019, ICLR.
[34] J. Lee,et al. Predicting What You Already Know Helps: Provable Self-Supervised Learning , 2020, NeurIPS.
[35] Oriol Vinyals,et al. Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML , 2019, ICLR.
[36] Michael I. Jordan,et al. Provable Meta-Learning of Linear Representations , 2020, ICML.