Dataset Meta-Learning from Kernel Ridge-Regression

30 October 2020

Papers citing "Dataset Meta-Learning from Kernel Ridge-Regression"

50 / 167 papers shown

Title
Improved Distribution Matching for Dataset Condensation Ganlong Zhao Guanbin Li Yipeng Qin Yizhou Yu DD 23 80 0 19 Jul 2023
Towards Trustworthy Dataset Distillation Shijie Ma Fei Zhu Zhen Cheng Xu-Yao Zhang DD 37 14 0 18 Jul 2023
Dataset Distillation Meets Provable Subset Selection M. Tukan Alaa Maalouf Margarita Osadchy DD 31 4 0 16 Jul 2023
Distilled Pruning: Using Synthetic Data to Win the Lottery Luke McDermott Daniel Cummings SyDa DD 34 1 0 07 Jul 2023
Kernels, Data & Physics Francesco Cagnetta Deborah Oliveira Mahalakshmi Sabanayagam Nikolaos Tsilivis Julia Kempe 25 0 0 05 Jul 2023
Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving Training Data Release for Machine Learning Tamas Madl Weijie Xu Olivia Choudhury Matthew Howard 21 5 0 04 Jul 2023
Large-scale Dataset Pruning with Dynamic Uncertainty Muyang He Shuo Yang Tiejun Huang Bo-Lu Zhao 36 25 0 08 Jun 2023
Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data Xin Zheng Miao Zhang C. Chen Quoc Viet Hung Nguyen Xingquan Zhu Shirui Pan DD 36 59 0 05 Jun 2023
Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching Tao Feng Jie Zhang Peizheng Wang Zhijie Wang Shengyuan Pang DD 46 0 0 29 May 2023
Distill Gold from Massive Ores: Efficient Dataset Distillation via Critical Samples Selection Yue Xu Yong-Lu Li Kaitong Cui Ziyu Wang Cewu Lu Yu-Wing Tai Chi-Keung Tang DD 33 8 0 28 May 2023
Summarizing Stream Data for Memory-Constrained Online Continual Learning Jianyang Gu Kai Wang Wei Jiang Yang You DD 24 9 0 26 May 2023
On the Size and Approximation Error of Distilled Sets Alaa Maalouf M. Tukan Noel Loo Ramin Hasani Mathias Lechner Daniela Rus DD 29 4 0 23 May 2023
A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness Zongxiong Chen Jiahui Geng Derui Zhu Herbert Woisetschlaeger Qing Li Sonja Schimmler Ruben Mayer Chunming Rong DD 24 9 0 05 May 2023
A Survey on Dataset Distillation: Approaches, Applications and Future Directions Jiahui Geng Zongxiong Chen Yuandou Wang Herbert Woisetschlaeger Sonja Schimmler Ruben Mayer Zhiming Zhao Chunming Rong DD 57 26 0 03 May 2023
Generalizing Dataset Distillation via Deep Generative Prior George Cazenavette Tongzhou Wang Antonio Torralba Alexei A. Efros Jun-Yan Zhu DD 91 84 0 02 May 2023
Bayesian Pseudo-Coresets via Contrastive Divergence Piyush Tiwary Kumar Shubham V. Kashyap Prathosh A.P. 21 3 0 20 Mar 2023
Kernel Regression with Infinite-Width Neural Networks on Millions of Examples Ben Adlam Jaehoon Lee Shreyas Padhy Zachary Nado Jasper Snoek 15 11 0 09 Mar 2023
InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning Ziheng Qin K. Wang Zangwei Zheng Jianyang Gu Xiang Peng ... Daquan Zhou Lei Shang Baigui Sun Xuansong Xie Yang You 116 46 0 08 Mar 2023
DiM: Distilling Dataset into Generative Model Kai Wang Jianyang Gu Daquan Zhou Zheng Hua Zhu Wei Jiang Yang You DD 51 40 0 08 Mar 2023
Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation Yilin Yang Kamil Adamczewski Danica J. Sutherland Xiaoxiao Li Mijung Park 33 14 0 03 Mar 2023
DREAM: Efficient Dataset Distillation by Representative Matching Yanqing Liu Jianyang Gu Kai Wang Zheng Hua Zhu Wei Jiang Yang You DD 31 76 0 28 Feb 2023
Dataset Distillation with Convexified Implicit Gradients Noel Loo Ramin Hasani Mathias Lechner Daniela Rus DD 29 41 0 13 Feb 2023
Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation Noel Loo Ramin Hasani Mathias Lechner Alexander Amini Daniela Rus DD 34 5 0 02 Feb 2023
Differentially Private Kernel Inducing Points using features from ScatterNets (DP-KIP-ScatterNet) for Privacy Preserving Data Distillation Margarita Vinaroz M. Park DD 20 0 0 31 Jan 2023
Probabilistic Bilevel Coreset Selection Xiao Zhou Renjie Pi Weizhong Zhang Yong Lin Tong Zhang NoLa 25 27 0 24 Jan 2023
Dataset Distillation: A Comprehensive Review Ruonan Yu Songhua Liu Xinchao Wang DD 41 121 0 17 Jan 2023
A Comprehensive Survey of Dataset Distillation Shiye Lei Dacheng Tao DD 31 87 0 13 Jan 2023
Data Distillation: A Survey Noveen Sachdeva Julian McAuley DD 45 73 0 11 Jan 2023
Backdoor Attacks Against Dataset Distillation Yugeng Liu Zheng Li Michael Backes Yun Shen Yang Zhang DD 34 27 0 03 Jan 2023
Accelerating Dataset Distillation via Model Augmentation Lei Zhang Jie M. Zhang Bowen Lei Subhabrata Mukherjee Xiang Pan Bo-Lu Zhao Caiwen Ding Y. Li Dongkuan Xu DD 40 62 0 12 Dec 2022
A Flexible Nadaraya-Watson Head Can Offer Explainable and Calibrated Classification Alan Q. Wang M. Sabuncu 30 5 0 07 Dec 2022
Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation Jiawei Du Yiding Jiang Vincent Y. F. Tan Joey Tianyi Zhou Haizhou Li DD 35 109 0 20 Nov 2022
Towards Robust Dataset Learning Yihan Wu Xinda Li Florian Kerschbaum Heng Huang Hongyang R. Zhang DD OOD 41 10 0 19 Nov 2022
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory Justin Cui Ruochen Wang Si Si Cho-Jui Hsieh DD 20 129 0 19 Nov 2022
Black-box Coreset Variational Inference Dionysis Manousakas H. Ritter Theofanis Karaletsos BDL 11 4 0 04 Nov 2022
Dataset Distillation via Factorization Songhua Liu Kai Wang Xingyi Yang Jingwen Ye Xinchao Wang DD 126 141 0 30 Oct 2022
Efficient Dataset Distillation Using Random Feature Approximation Noel Loo Ramin Hasani Alexander Amini Daniela Rus DD 69 95 0 21 Oct 2022
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities Brian Bartoldson B. Kailkhura Davis W. Blalock 31 47 0 13 Oct 2022
On Divergence Measures for Bayesian Pseudocoresets Balhae Kim J. Choi Seanie Lee Yoonho Lee Jung-Woo Ha Juho Lee DD 8 11 0 12 Oct 2022
Few-shot Backdoor Attacks via Neural Tangent Kernels J. Hayase Sewoong Oh 30 21 0 12 Oct 2022
Fast Neural Kernel Embeddings for General Activations Insu Han A. Zandieh Jaehoon Lee Roman Novak Lechao Xiao Amin Karbasi 48 18 0 09 Sep 2022
Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments Rui Song Dai Liu Da Chen Andreas Festag Carsten Trinitis Martin Schulz Alois C. Knoll DD FedML 23 61 0 24 Aug 2022
Dataset Condensation with Latent Space Knowledge Factorization and Sharing Haebeom Lee Dong Bok Lee Sung Ju Hwang DD 21 37 0 21 Aug 2022
Delving into Effective Gradient Matching for Dataset Condensation Zixuan Jiang Jiaqi Gu Mingjie Liu D. Pan DD 20 41 0 30 Jul 2022
Can we achieve robustness from data alone? Nikolaos Tsilivis Jingtong Su Julia Kempe OOD DD 36 18 0 24 Jul 2022
Gradient-based Bi-level Optimization for Deep Learning: A Survey Can Chen Xiangshan Chen Chen-li Ma Zixuan Liu Xue Liu 84 35 0 24 Jul 2022
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning Yuanhao Xiong Ruochen Wang Minhao Cheng Felix X. Yu Cho-Jui Hsieh FedML DD 47 82 0 20 Jul 2022
DC-BENCH: Dataset Condensation Benchmark Justin Cui Ruochen Wang Si Si Cho-Jui Hsieh DD 31 72 0 20 Jul 2022
A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel Mohamad Amin Mohamadi Wonho Bae Danica J. Sutherland AAML 29 27 0 25 Jun 2022
Fast Finite Width Neural Tangent Kernel Roman Novak Jascha Narain Sohl-Dickstein S. Schoenholz AAML 18 52 0 17 Jun 2022