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 / 315 papers shown

Title
Efficient Quantum State Sample Tomography with Basis-dependent Neural-networks Alistair W. R. Smith Johnnie Gray M. S. Kim 11 28 0 16 Sep 2020
Minimum discrepancy principle strategy for choosing $k$ in $k$ -NN regression Yaroslav Averyanov Alain Celisse 18 0 0 20 Aug 2020
Prevalence of Neural Collapse during the terminal phase of deep learning training Vardan Papyan Xuemei Han D. Donoho 35 549 0 18 Aug 2020
The Interpolation Phase Transition in Neural Networks: Memorization and Generalization under Lazy Training Andrea Montanari Yiqiao Zhong 49 95 0 25 Jul 2020
What shapes feature representations? Exploring datasets, architectures, and training Katherine L. Hermann Andrew Kyle Lampinen OOD 23 153 0 22 Jun 2020
Exploring Weight Importance and Hessian Bias in Model Pruning Mingchen Li Yahya Sattar Christos Thrampoulidis Samet Oymak 28 3 0 19 Jun 2020
When Does Preconditioning Help or Hurt Generalization? S. Amari Jimmy Ba Roger C. Grosse Xuechen Li Atsushi Nitanda Taiji Suzuki Denny Wu Ji Xu 36 32 0 18 Jun 2020
Interpolation and Learning with Scale Dependent Kernels Nicolò Pagliana Alessandro Rudi E. De Vito Lorenzo Rosasco 44 8 0 17 Jun 2020
Directional Pruning of Deep Neural Networks Shih-Kang Chao Zhanyu Wang Yue Xing Guang Cheng ODL 21 33 0 16 Jun 2020
On the training dynamics of deep networks with $L_2$ regularization Aitor Lewkowycz Guy Gur-Ari 44 53 0 15 Jun 2020
Assumption-lean inference for generalised linear model parameters S. Vansteelandt O. Dukes CML 27 49 0 15 Jun 2020
Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks Yehuda Dar Richard G. Baraniuk 36 19 0 12 Jun 2020
To Each Optimizer a Norm, To Each Norm its Generalization Sharan Vaswani Reza Babanezhad Jose Gallego Aaron Mishkin Simon Lacoste-Julien Nicolas Le Roux 26 8 0 11 Jun 2020
Double Descent Risk and Volume Saturation Effects: A Geometric Perspective Prasad Cheema M. Sugiyama 14 3 0 08 Jun 2020
An Overview of Neural Network Compression James OÑeill AI4CE 45 98 0 05 Jun 2020
Machine Learning-Based Unbalance Detection of a Rotating Shaft Using Vibration Data Oliver Mey Willi Neudeck André Schneider Olaf Enge-Rosenblatt 6 28 0 26 May 2020
Is deeper better? It depends on locality of relevant features Takashi Mori Masahito Ueda OOD 25 4 0 26 May 2020
Gradient Monitored Reinforcement Learning Mohammed Sharafath Abdul Hameed Gavneet Singh Chadha Andreas Schwung S. Ding 33 10 0 25 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 41 149 0 16 May 2020
An Investigation of Why Overparameterization Exacerbates Spurious Correlations Shiori Sagawa Aditi Raghunathan Pang Wei Koh Percy Liang 152 371 0 09 May 2020
Determinantal Point Processes in Randomized Numerical Linear Algebra Michal Derezinski Michael W. Mahoney 34 77 0 07 May 2020
Understanding and Improving Information Transfer in Multi-Task Learning Sen Wu Hongyang R. Zhang Christopher Ré 18 154 0 02 May 2020
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond Fanghui Liu Xiaolin Huang Yudong Chen Johan A. K. Suykens BDL 44 172 0 23 Apr 2020
Going in circles is the way forward: the role of recurrence in visual inference R. S. V. Bergen N. Kriegeskorte 17 82 0 26 Mar 2020
Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime Stéphane dÁscoli Maria Refinetti Giulio Biroli Florent Krzakala 93 152 0 02 Mar 2020
Loss landscapes and optimization in over-parameterized non-linear systems and neural networks Chaoyue Liu Libin Zhu M. Belkin ODL 17 247 0 29 Feb 2020
Overfitting in adversarially robust deep learning Leslie Rice Eric Wong Zico Kolter 47 785 0 26 Feb 2020
The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization Yifei Min Lin Chen Amin Karbasi AAML 37 69 0 25 Feb 2020
Coherent Gradients: An Approach to Understanding Generalization in Gradient Descent-based Optimization S. Chatterjee ODL OOD 11 48 0 25 Feb 2020
Generalisation error in learning with random features and the hidden manifold model Federica Gerace Bruno Loureiro Florent Krzakala M. Mézard Lenka Zdeborová 25 165 0 21 Feb 2020
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nyström method Michal Derezinski Rajiv Khanna Michael W. Mahoney 29 10 0 21 Feb 2020
Bayesian Deep Learning and a Probabilistic Perspective of Generalization A. Wilson Pavel Izmailov UQCV BDL OOD 24 639 0 20 Feb 2020
Implicit Regularization of Random Feature Models Arthur Jacot Berfin Simsek Francesco Spadaro Clément Hongler Franck Gabriel 31 82 0 19 Feb 2020
Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data Charles H. Martin Tongsu Peng Peng Michael W. Mahoney 36 101 0 17 Feb 2020
Estimating Uncertainty Intervals from Collaborating Networks Tianhui Zhou Yitong Li Yuan Wu David Carlson UQCV 30 15 0 12 Feb 2020
Sparse Recovery With Non-Linear Fourier Features Ayça Özçelikkale 19 5 0 12 Feb 2020
Asymptotic errors for convex penalized linear regression beyond Gaussian matrices Cédric Gerbelot A. Abbara Florent Krzakala 34 16 0 11 Feb 2020
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks Blake Bordelon Abdulkadir Canatar Cengiz Pehlevan 146 201 0 07 Feb 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 38 300 0 08 Jan 2020
From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems Osvaldo Simeone Sangwoo Park Joonhyuk Kang AI4CE 31 62 0 05 Jan 2020
Machine Learning from a Continuous Viewpoint E. Weinan Chao Ma Lei Wu 27 102 0 30 Dec 2019
Optimization for deep learning: theory and algorithms Ruoyu Sun ODL 25 168 0 19 Dec 2019
Exact expressions for double descent and implicit regularization via surrogate random design Michal Derezinski Feynman T. Liang Michael W. Mahoney 27 77 0 10 Dec 2019
In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors Jeffrey Negrea Gintare Karolina Dziugaite Daniel M. Roy AI4CE 40 64 0 09 Dec 2019
A Model of Double Descent for High-dimensional Binary Linear Classification Zeyu Deng A. Kammoun Christos Thrampoulidis 36 145 0 13 Nov 2019
Rate-Regularization and Generalization in VAEs Alican Bozkurt Babak Esmaeili Jean-Baptiste Tristan Dana H. Brooks Jennifer G. Dy Jan-Willem van de Meent DRL 25 7 0 11 Nov 2019
Capacity, Bandwidth, and Compositionality in Emergent Language Learning Cinjon Resnick Abhinav Gupta Jakob N. Foerster Andrew M. Dai Kyunghyun Cho 20 51 0 24 Oct 2019
Predicting materials properties without crystal structure: Deep representation learning from stoichiometry Rhys E. A. Goodall A. Lee 18 253 0 01 Oct 2019
Overparameterized Neural Networks Implement Associative Memory Adityanarayanan Radhakrishnan M. Belkin Caroline Uhler BDL 35 71 0 26 Sep 2019
Finite Depth and Width Corrections to the Neural Tangent Kernel Boris Hanin Mihai Nica MDE 27 150 0 13 Sep 2019