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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 / 938 papers shown

Title
Generalization vs. Specialization under Concept Shift Alex Nguyen David J. Schwab Vudtiwat Ngampruetikorn OOD 196 0 0 23 Sep 2024
Monomial Matrix Group Equivariant Neural Functional NetworksNeural Information Processing Systems (NeurIPS), 2024 Hoang V. Tran Thieu N. Vo Tho H. Tran An T. Nguyen Tan M. Nguyen 403 12 0 18 Sep 2024
Unified Neural Network Scaling Laws and Scale-time Equivalence Akhilan Boopathy Ila Fiete 422 1 0 09 Sep 2024
Breaking Neural Network Scaling Laws with ModularityInternational Conference on Learning Representations (ICLR), 2024 Akhilan Boopathy Sunshine Jiang William Yue Jaedong Hwang Abhiram Iyer Ila Fiete OOD 348 6 0 09 Sep 2024
NGD converges to less degenerate solutions than SGD Moosa Saghir N. R. Raghavendra Zihe Liu Evan Ryan Gunter 156 0 0 07 Sep 2024
Theoretical Insights into Overparameterized Models in Multi-Task and Replay-Based Continual Learning Mohammadamin Banayeeanzade Mahdi Soltanolkotabi Mohammad Rostami CLL LRM 505 5 0 29 Aug 2024
Optimal Kernel Quantile Learning with Random FeaturesInternational Conference on Machine Learning (ICML), 2024 Caixing Wang Xingdong Feng 340 2 0 24 Aug 2024
Approaching Deep Learning through the Spectral Dynamics of Weights David Yunis Kumar Kshitij Patel Samuel Wheeler Pedro H. P. Savarese Gal Vardi Karen Livescu Michael Maire Matthew R. Walter 262 12 0 21 Aug 2024
On the effect of noise on fitting linear regression models Insha Ullah A. H. Welsh 116 0 0 15 Aug 2024
Operator Learning Using Random Features: A Tool for Scientific ComputingSIAM Review (SIAM Rev.), 2024 Nicholas H. Nelsen Andrew M. Stuart 237 20 0 12 Aug 2024
Generalization bounds for regression and classification on adaptive covering input domains Wen-Liang Hwang 174 0 0 29 Jul 2024
$u-$\mu$P: The Unit-Scaled Maximal Update Parametrization$ u- $\mu$ P: The Unit-Scaled Maximal Update Parametrization Charlie Blake C. Eichenberg Josef Dean Lukas Balles Luke Y. Prince Bjorn Deiseroth Andres Felipe Cruz Salinas Carlo Luschi Samuel Weinbach Douglas Orr 224 16 0 24 Jul 2024
Can all variations within the unified mask-based beamformer framework achieve identical peak extraction performance? Atsuo Hiroe Katsutoshi Itoyama Kazuhiro Nakadai 191 0 0 22 Jul 2024
Towards understanding epoch-wise double descent in two-layer linear neural networks Amanda Olmin Fredrik Lindsten MLT 213 4 0 13 Jul 2024
How more data can hurt: Instability and regularization in next-generation reservoir computing Yuanzhao Zhang Edmilson Roque dos Santos Huixin Zhang Sean P. Cornelius 379 3 0 11 Jul 2024
One system for learning and remembering episodes and rules Joshua T. S. Hewson Sabina J. Sloman Marina Dubova CLL 127 0 0 08 Jul 2024
How DNNs break the Curse of Dimensionality: Compositionality and Symmetry Learning Arthur Jacot Seok Hoan Choi Yuxiao Wen AI4CE 295 5 0 08 Jul 2024
Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networks Amit Peleg Matthias Hein 223 0 0 04 Jul 2024
Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation Amartya Sanyal Yaxi Hu Yaodong Yu Yian Ma Yixin Wang Bernhard Schölkopf OODD 180 7 0 27 Jun 2024
Multi-Epoch learning with Data Augmentation for Deep Click-Through Rate Prediction Zhongxiang Fan Zhaocheng Liu Jian Liang Dongying Kong Han Li Peng Jiang Shuang Li Kun Gai 195 1 0 27 Jun 2024
Coding schemes in neural networks learning classification tasks Alexander van Meegen H. Sompolinsky 178 17 0 24 Jun 2024
MD tree: a model-diagnostic tree grown on loss landscape Yefan Zhou Jianlong Chen Qinxue Cao Konstantin Schürholt Yaoqing Yang 259 2 0 24 Jun 2024
The Right Time Matters: Data Arrangement Affects Zero-Shot Generalization in Instruction Tuning Bingxiang He Ning Ding Cheng Qian Jia Deng Ganqu Cui ... Longtao Huang Hui Xue Huimin Chen Zhiyuan Liu Maosong Sun 162 2 0 17 Jun 2024
An Efficient Approach to Regression Problems with Tensor Neural Networks Yongxin Li 41 0 0 14 Jun 2024
Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis Zhang Chen Christian Scano Srishti Gupta Xiaoyi Feng Zhaoqiang Xia ... Maura Pintor Luca Oneto Ambra Demontis Battista Biggio Fabio Roli AAML 305 2 0 14 Jun 2024
Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations Rylan Schaeffer Victor Lecomte Dhruv Pai Andres Carranza Berivan Isik ... Yann LeCun SueYeon Chung Andrey Gromov Ravid Shwartz-Ziv Sanmi Koyejo 241 9 0 13 Jun 2024
Precise analysis of ridge interpolators under heavy correlations -- a Random Duality Theory view Mihailo Stojnic 180 1 0 13 Jun 2024
Ridge interpolators in correlated factor regression models -- exact risk analysis Mihailo Stojnic 152 1 0 13 Jun 2024
Assessment of Uncertainty Quantification in Universal Differential Equations Nina Schmid David Fernandes del Pozo Willem Waegeman Jan Hasenauer AI4CE 272 7 0 13 Jun 2024
Probing Implicit Bias in Semi-gradient Q-learning: Visualizing the Effective Loss Landscapes via the Fokker--Planck Equation Shuyu Yin Fei Wen Peilin Liu Tao Luo 223 0 0 12 Jun 2024
Optimal Recurrent Network Topologies for Dynamical Systems ReconstructionInternational Conference on Machine Learning (ICML), 2024 Christoph Jürgen Hemmer Manuel Brenner Florian Hess Daniel Durstewitz 214 5 0 07 Jun 2024
Federated Representation Learning in the Under-Parameterized RegimeInternational Conference on Machine Learning (ICML), 2024 Renpu Liu Cong Shen Jing Yang 254 8 0 07 Jun 2024
Error Bounds of Supervised Classification from Information-Theoretic Perspective Binchuan Qi Wei Gong Li Li 215 0 0 07 Jun 2024
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech EnhancementInterspeech (Interspeech), 2024 Wangyou Zhang Kohei Saijo Jee-weon Jung Chenda Li Shinji Watanabe Yanmin Qian 157 16 0 06 Jun 2024
Data Quality in Edge Machine Learning: A State-of-the-Art Survey M. D. Belgoumri Mohamed Reda Bouadjenek Sunil Aryal Hakim Hacid 272 2 0 01 Jun 2024
Grokfast: Accelerated Grokking by Amplifying Slow Gradients Jaerin Lee Bong Gyun Kang Kihoon Kim Kyoung Mu Lee 230 21 0 30 May 2024
A Margin-based Multiclass Generalization Bound via Geometric Complexity Michael Munn Benoit Dherin Javier Gonzalvo UQCV 190 2 0 28 May 2024
Is machine learning good or bad for the natural sciences? David W. Hogg Soledad Villar AI4CE 281 10 0 28 May 2024
Phase Transitions in the Output Distribution of Large Language Models Julian Arnold Flemming Holtorf Frank Schafer Niels Lörch 203 3 0 27 May 2024
Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers Lorenzo Tiberi Francesca Mignacco Kazuki Irie H. Sompolinsky 331 9 0 24 May 2024
Entrywise error bounds for low-rank approximations of kernel matricesNeural Information Processing Systems (NeurIPS), 2024 Alexander Modell 234 0 0 23 May 2024
When predict can also explain: few-shot prediction to select better neural latents Kabir V. Dabholkar Omri Barak BDL 342 0 0 23 May 2024
Asymptotic theory of in-context learning by linear attention Yue M. Lu Mary I. Letey Jacob A. Zavatone-Veth Anindita Maiti Cengiz Pehlevan 458 38 0 20 May 2024
The fast committor machine: Interpretable prediction with kernelsJournal of Chemical Physics (JCP), 2024 D. Aristoff Mats S. Johnson Gideon Simpson Robert J. Webber 218 9 0 16 May 2024
Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory Xueyan Niu Bo Bai Lei Deng Wei Han 169 14 0 14 May 2024
Scalable Subsampling Inference for Deep Neural NetworksACM / IMS Journal of Data Science (JIDS), 2024 Kejin Wu D. Politis 135 3 0 14 May 2024
Class-wise Activation Unravelling the Engima of Deep Double Descent Yufei Gu 110 0 0 13 May 2024
Data-Error Scaling Laws in Machine Learning on Combinatorial Mutation-prone Sets: Proteins and Small Molecules Vanni Doffini O. A. von Lilienfeld Michael A. Nash 160 1 0 08 May 2024
Finite Sample Analysis and Bounds of Generalization Error of Gradient Descent in In-Context Linear Regression Karthik Duraisamy MLT 255 4 0 03 May 2024
Position: Why We Must Rethink Empirical Research in Machine LearningInternational Conference on Machine Learning (ICML), 2024 Moritz Herrmann F. J. D. Lange Katharina Eggensperger Giuseppe Casalicchio Marcel Wever Matthias Feurer David Rügamer Eyke Hüllermeier A. Boulesteix B. Bischl 228 19 0 03 May 2024