$$\ell_1$-regularized Neural Networks are Improperly Learnable in Polynomial Time$

$\ell_1$ -regularized Neural Networks are Improperly Learnable in Polynomial Time

13 October 2015

Papers citing "$\ell_1$-regularized Neural Networks are Improperly Learnable in Polynomial Time"

50 / 67 papers shown

Title
Learning Neural Networks with Distribution Shift: Efficiently Certifiable Guarantees Gautam Chandrasekaran Adam R. Klivans Lin Lin Lee Konstantinos Stavropoulos OOD 40 0 0 22 Feb 2025
A New Random Reshuffling Method for Nonsmooth Nonconvex Finite-sum Optimization Junwen Qiu Xiao Li Andre Milzarek 39 2 0 02 Dec 2023
On the Convergence and Sample Complexity Analysis of Deep Q-Networks with $ε$ -Greedy Exploration Shuai Zhang Hongkang Li Meng Wang Miao Liu Pin-Yu Chen Songtao Lu Sijia Liu K. Murugesan Subhajit Chaudhury 40 19 0 24 Oct 2023
Efficiently Learning One-Hidden-Layer ReLU Networks via Schur Polynomials Ilias Diakonikolas D. Kane 32 4 0 24 Jul 2023
A faster and simpler algorithm for learning shallow networks Sitan Chen Shyam Narayanan 41 7 0 24 Jul 2023
Most Neural Networks Are Almost Learnable Amit Daniely Nathan Srebro Gal Vardi 26 0 0 25 May 2023
Learning Narrow One-Hidden-Layer ReLU Networks Sitan Chen Zehao Dou Surbhi Goel Adam R. Klivans Raghu Meka MLT 24 13 0 20 Apr 2023
Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy Amit Daniely Nathan Srebro Gal Vardi 33 4 0 15 Feb 2023
Is Stochastic Gradient Descent Near Optimal? Yifan Zhu Hong Jun Jeon Benjamin Van Roy 25 2 0 18 Sep 2022
Statistical Guarantees for Approximate Stationary Points of Simple Neural Networks Mahsa Taheri Fang Xie Johannes Lederer 29 0 0 09 May 2022
How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis Shuai Zhang Ming Wang Sijia Liu Pin-Yu Chen Jinjun Xiong SSL MLT 41 22 0 21 Jan 2022
A Survey on Interpretable Reinforcement Learning Claire Glanois Paul Weng Matthieu Zimmer Dong Li Tianpei Yang Jianye Hao Wulong Liu OffRL 23 95 0 24 Dec 2021
Learning by Active Forgetting for Neural Networks Jian Peng Xian Sun Min Deng Chao Tao Bo Tang ... Guohua Wu Qing Zhu Yu Liu Tao R. Lin Haifeng Li CLL KELM AI4CE 26 3 0 21 Nov 2021
A spectral-based analysis of the separation between two-layer neural networks and linear methods Lei Wu Jihao Long 18 8 0 10 Aug 2021
Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable Models Ilias Diakonikolas D. Kane 21 32 0 14 Dec 2020
Learning Graph Neural Networks with Approximate Gradient Descent Qunwei Li Shaofeng Zou Leon Wenliang Zhong GNN 32 1 0 07 Dec 2020
Learning Deep ReLU Networks Is Fixed-Parameter Tractable Sitan Chen Adam R. Klivans Raghu Meka 22 36 0 28 Sep 2020
From Boltzmann Machines to Neural Networks and Back Again Surbhi Goel Adam R. Klivans Frederic Koehler 19 5 0 25 Jul 2020
Graph Neural Networks Including Sparse Interpretability Chris Lin Gerald J. Sun K. Bulusu J. Dry Marylens Hernandez 11 7 0 30 Jun 2020
Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks Ilias Diakonikolas D. Kane Vasilis Kontonis Nikos Zarifis 14 65 0 22 Jun 2020
Statistical Guarantees for Regularized Neural Networks Mahsa Taheri Fang Xie Johannes Lederer 52 38 0 30 May 2020
Harmonic Decompositions of Convolutional Networks M. Scetbon Zaïd Harchaoui 25 7 0 28 Mar 2020
A Spectral Analysis of Dot-product Kernels M. Scetbon Zaïd Harchaoui 182 2 0 28 Feb 2020
Generalised Lipschitz Regularisation Equals Distributional Robustness Zac Cranko Zhan Shi Xinhua Zhang Richard Nock Simon Kornblith OOD 20 20 0 11 Feb 2020
Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Distillation Junjie Liu Dongchao Wen Hongxing Gao Wei Tao Tse-Wei Chen Kinya Osa Masami Kato 22 21 0 13 Nov 2019
Renyi Differentially Private ADMM for Non-Smooth Regularized Optimization Chen Chen Jaewoo Lee 17 3 0 18 Sep 2019
Optimizing for Interpretability in Deep Neural Networks with Tree Regularization Mike Wu S. Parbhoo M. C. Hughes Volker Roth Finale Doshi-Velez AI4CE 20 27 0 14 Aug 2019
Stochastic In-Face Frank-Wolfe Methods for Non-Convex Optimization and Sparse Neural Network Training Paul Grigas Alfonso Lobos Nathan Vermeersch 19 5 0 09 Jun 2019
Learning Representations of Graph Data -- A Survey Mital Kinderkhedia GNN 17 12 0 07 Jun 2019
What Can ResNet Learn Efficiently, Going Beyond Kernels? Zeyuan Allen-Zhu Yuanzhi Li 24 183 0 24 May 2019
On the Power and Limitations of Random Features for Understanding Neural Networks Gilad Yehudai Ohad Shamir MLT 26 181 0 01 Apr 2019
Tensor Dropout for Robust Learning Arinbjorn Kolbeinsson Jean Kossaifi Yannis Panagakis Adrian Bulat Anima Anandkumar I. Tzoulaki Paul Matthews OOD 28 2 0 27 Feb 2019
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers Zeyuan Allen-Zhu Yuanzhi Li Yingyu Liang MLT 14 765 0 12 Nov 2018
Learning Two Layer Rectified Neural Networks in Polynomial Time Ainesh Bakshi Rajesh Jayaram David P. Woodruff NoLa 15 69 0 05 Nov 2018
On the Convergence Rate of Training Recurrent Neural Networks Zeyuan Allen-Zhu Yuanzhi Li Zhao Song 23 191 0 29 Oct 2018
Rademacher Complexity for Adversarially Robust Generalization Dong Yin Kannan Ramchandran Peter L. Bartlett AAML 24 257 0 29 Oct 2018
Learning Two-layer Neural Networks with Symmetric Inputs Rong Ge Rohith Kuditipudi Zhize Li Xiang Wang OOD MLT 36 57 0 16 Oct 2018
An ETF view of Dropout regularization Dor Bank Raja Giryes 8 4 0 14 Oct 2018
Principled Deep Neural Network Training through Linear Programming D. Bienstock Gonzalo Muñoz Sebastian Pokutta 35 24 0 07 Oct 2018
A Kernel Perspective for Regularizing Deep Neural Networks A. Bietti Grégoire Mialon Dexiong Chen Julien Mairal 11 15 0 30 Sep 2018
Learning Restricted Boltzmann Machines via Influence Maximization Guy Bresler Frederic Koehler Ankur Moitra Elchanan Mossel AI4CE 20 29 0 25 May 2018
A Mean Field View of the Landscape of Two-Layers Neural Networks Song Mei Andrea Montanari Phan-Minh Nguyen MLT 43 850 0 18 Apr 2018
A comparison of deep networks with ReLU activation function and linear spline-type methods Konstantin Eckle Johannes Schmidt-Hieber 17 322 0 06 Apr 2018
A Provably Correct Algorithm for Deep Learning that Actually Works Eran Malach Shai Shalev-Shwartz MLT 18 30 0 26 Mar 2018
On the Connection Between Learning Two-Layers Neural Networks and Tensor Decomposition Marco Mondelli Andrea Montanari MLT CML 15 58 0 20 Feb 2018
Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks Peter L. Bartlett D. Helmbold Philip M. Long 36 116 0 16 Feb 2018
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability Mike Wu M. C. Hughes S. Parbhoo Maurizio Zazzi Volker Roth Finale Doshi-Velez AI4CE 28 281 0 16 Nov 2017
Learning One-hidden-layer Neural Networks with Landscape Design Rong Ge J. Lee Tengyu Ma MLT 29 260 0 01 Nov 2017
Porcupine Neural Networks: (Almost) All Local Optima are Global S. Feizi Hamid Javadi Jesse M. Zhang David Tse 20 36 0 05 Oct 2017
When is a Convolutional Filter Easy To Learn? S. Du J. Lee Yuandong Tian MLT 15 130 0 18 Sep 2017