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Reconciling modern machine learning practice and the bias-variance trade-off

28 December 2018

Papers citing "Reconciling modern machine learning practice and the bias-variance trade-off"

50 / 942 papers shown

Title
Ensemble Kernel Methods, Implicit Regularization and Determinantal Point Processes J. Schreurs Michaël Fanuel Johan A. K. Suykens 263 2 0 24 Jun 2020
Spectral Bias and Task-Model Alignment Explain Generalization in Kernel Regression and Infinitely Wide Neural NetworksNature Communications (Nat Commun), 2020 Abdulkadir Canatar Blake Bordelon Cengiz Pehlevan 503 221 0 23 Jun 2020
Good Classifiers are Abundant in the Interpolating Regime Ryan Theisen Jason M. Klusowski Michael W. Mahoney 143 2 0 22 Jun 2020
What shapes feature representations? Exploring datasets, architectures, and training Katherine L. Hermann Andrew Kyle Lampinen OOD 240 174 0 22 Jun 2020
Statistical and Algorithmic Insights for Semi-supervised Learning with Self-training Samet Oymak Talha Cihad Gulcu 181 22 0 19 Jun 2020
Exploring Weight Importance and Hessian Bias in Model Pruning Mingchen Li Yahya Sattar Christos Thrampoulidis Samet Oymak 178 4 0 19 Jun 2020
MARS: Masked Automatic Ranks Selection in Tensor Decompositions M. Kodryan D. Kropotov Dmitry Vetrov 290 10 0 18 Jun 2020
The Recurrent Neural Tangent Kernel Sina Alemohammad Zichao Wang Randall Balestriero Richard Baraniuk AAML 183 81 0 18 Jun 2020
On Sparsity in Overparametrised Shallow ReLU Networks Jaume de Dios Joan Bruna 117 14 0 18 Jun 2020
Revisiting minimum description length complexity in overparameterized models Raaz Dwivedi Chandan Singh Bin Yu Martin J. Wainwright 403 5 0 17 Jun 2020
Interpolation and Learning with Scale Dependent Kernels Nicolò Pagliana Alessandro Rudi Ernesto De Vito Lorenzo Rosasco 287 8 0 17 Jun 2020
Directional Pruning of Deep Neural Networks Shih-Kang Chao Zhanyu Wang Yue Xing Guang Cheng ODL 207 35 0 16 Jun 2020
On the training dynamics of deep networks with $L_2$ regularization Aitor Lewkowycz Guy Gur-Ari 261 63 0 15 Jun 2020
The role of optimization geometry in single neuron learning Nicholas M. Boffi Stephen Tu Jean-Jacques E. Slotine 120 1 0 15 Jun 2020
Overparameterization and generalization error: weighted trigonometric interpolation Yuege Xie H. Chou Holger Rauhut Rachel A. Ward 90 3 0 15 Jun 2020
Assumption-lean inference for generalised linear model parameters S. Vansteelandt O. Dukes CML 274 65 0 15 Jun 2020
Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression TasksSIAM Journal on Mathematics of Data Science (SIMODS), 2020 Yehuda Dar Richard G. Baraniuk 415 20 0 12 Jun 2020
To Each Optimizer a Norm, To Each Norm its Generalization Sharan Vaswani Reza Babanezhad Jose Gallego Aaron Mishkin Damien Scieur Nicolas Le Roux 104 8 0 11 Jun 2020
Is deep learning necessary for simple classification tasks?Genetic Programming and Evolvable Machines (GPEM), 2020 Joseph D. Romano Trang T. Le Weixuan Fu J. Moore 221 22 0 11 Jun 2020
Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimizationNeural Information Processing Systems (NeurIPS), 2020 Benjamin Aubin Florent Krzakala Yue M. Lu Lenka Zdeborová 240 62 0 11 Jun 2020
Asymptotics of Ridge (less) Regression under General Source ConditionInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020 Dominic Richards Jaouad Mourtada Lorenzo Rosasco 229 82 0 11 Jun 2020
Recovery and Generalization in Over-Realized Dictionary LearningJournal of machine learning research (JMLR), 2020 Jeremias Sulam Chong You Zhihui Zhu FedML 224 8 0 11 Jun 2020
A Random Matrix Analysis of Random Fourier Features: Beyond the Gaussian Kernel, a Precise Phase Transition, and the Corresponding Double Descent Zhenyu Liao Romain Couillet Michael W. Mahoney 189 96 0 09 Jun 2020
Double Descent Risk and Volume Saturation Effects: A Geometric Perspective Prasad Cheema M. Sugiyama 228 3 0 08 Jun 2020
An Overview of Neural Network Compression James OÑeill AI4CE 288 111 0 05 Jun 2020
Triple descent and the two kinds of overfitting: Where & why do they appear? Stéphane dÁscoli Levent Sagun Giulio Biroli 210 84 0 05 Jun 2020
Ridge Regularizaton: an Essential Concept in Data Science Trevor Hastie 130 99 0 30 May 2020
On lower bounds for the bias-variance trade-offAnnals of Statistics (Ann. Stat.), 2020 A. Derumigny Johannes Schmidt-Hieber 354 9 0 30 May 2020
Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures Fanghui Liu Xiaolin Huang Yingyi Chen Johan A. K. Suykens 198 0 0 30 May 2020
Machine Learning-Based Unbalance Detection of a Rotating Shaft Using Vibration DataIEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2020 Oliver Mey Willi Neudeck André Schneider Olaf Enge-Rosenblatt 116 35 0 26 May 2020
Is deeper better? It depends on locality of relevant features Takashi Mori Masahito Ueda OOD 179 5 0 26 May 2020
Gradient Monitored Reinforcement LearningIEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2020 Mohammed Sharafath Abdul Hameed Gavneet Singh Chadha Andreas Schwung S. Ding 148 13 0 25 May 2020
The Random Feature Model for Input-Output Maps between Banach Spaces Nicholas H. Nelsen Andrew M. Stuart 280 162 0 20 May 2020
Classification vs regression in overparameterized regimes: Does the loss function matter? Vidya Muthukumar Adhyyan Narang Vignesh Subramanian M. Belkin Daniel J. Hsu A. Sahai 254 165 0 16 May 2020
Deep Ensembles on a Fixed Memory Budget: One Wide Network or Several Thinner Ones? Nadezhda Chirkova E. Lobacheva Dmitry Vetrov OOD MoE 93 9 0 14 May 2020
An Investigation of Why Overparameterization Exacerbates Spurious Correlations Shiori Sagawa Aditi Raghunathan Pang Wei Koh Abigail Z. Jacobs 488 409 0 09 May 2020
Determinantal Point Processes in Randomized Numerical Linear Algebra Michal Derezinski Michael W. Mahoney 194 91 0 07 May 2020
A framework for probabilistic weather forecast post-processing across models and lead times using machine learning Charlie Kirkwood T. Economou H. Odbert N. Pugeault 100 34 0 06 May 2020
Understanding and Improving Information Transfer in Multi-Task LearningInternational Conference on Learning Representations (ICLR), 2020 Sen Wu Hongyang R. Zhang Christopher Ré 180 173 0 02 May 2020
Generalization Error of Generalized Linear Models in High DimensionsInternational Conference on Machine Learning (ICML), 2020 M Motavali Emami Mojtaba Sahraee-Ardakan Parthe Pandit S. Rangan A. Fletcher AI4CE 111 40 0 01 May 2020
Generalization Error for Linear Regression under Distributed LearningInternational Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2020 Martin Hellkvist Ayça Özçelikkale Anders Ahlén FedML 94 6 0 30 Apr 2020
Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized RegimeJournal of machine learning research (JMLR), 2020 Niladri S. Chatterji Philip M. Long 193 114 0 25 Apr 2020
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond Fanghui Liu Xiaolin Huang Yudong Chen Johan A. K. Suykens BDL 413 188 0 23 Apr 2020
Mehler's Formula, Branching Process, and Compositional Kernels of Deep Neural NetworksJournal of the American Statistical Association (JASA), 2020 Tengyuan Liang Hai Tran-Bach 136 11 0 09 Apr 2020
A Brief Prehistory of Double DescentProceedings of the National Academy of Sciences of the United States of America (PNAS), 2020 Marco Loog T. Viering A. Mey Jesse H. Krijthe David Tax 132 74 0 07 Apr 2020
Going in circles is the way forward: the role of recurrence in visual inferenceCurrent Opinion in Neurobiology (Curr Opin Neurobiol), 2020 R. S. V. Bergen N. Kriegeskorte 288 90 0 26 Mar 2020
Dimension Independent Generalization Error by Stochastic Gradient Descent Xi Chen Qiang Liu Xin T. Tong 120 1 0 25 Mar 2020
Neural Networks and Polynomial Regression. Demystifying the Overparametrization Phenomena Matt Emschwiller D. Gamarnik Eren C. Kizildag Ilias Zadik 142 9 0 23 Mar 2020
Weak and Strong Gradient Directions: Explaining Memorization, Generalization, and Hardness of Examples at Scale Piotr Zielinski Shankar Krishnan S. Chatterjee ODL 200 2 0 16 Mar 2020
BayesFlow: Learning complex stochastic models with invertible neural networksIEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2020 Stefan T. Radev U. Mertens A. Voss Lynton Ardizzone Ullrich Kothe BDL 584 241 0 13 Mar 2020