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Geometric Median Matching for Robust k-Subset Selection from Noisy Data
v1v2 (latest)

Geometric Median Matching for Robust k-Subset Selection from Noisy Data

1 April 2025
Anish Acharya
Sujay Sanghavi
Alexandros G. Dimakis
Inderjit S Dhillon
    AAML
ArXiv (abs)PDFHTMLGithub

Papers citing "Geometric Median Matching for Robust k-Subset Selection from Noisy Data"

49 / 49 papers shown
Pareto Optimization with Robust Evaluation for Noisy Subset Selection
Pareto Optimization with Robust Evaluation for Noisy Subset Selection
Yi-Heng Xu
Dan-Xuan Liu
Chao Qian
Weiyong Yang
Chao Qian
NoLa
130
1
0
12 Jan 2025
Robust Data Pruning under Label Noise via Maximizing Re-labeling
  Accuracy
Robust Data Pruning under Label Noise via Maximizing Re-labeling AccuracyNeural Information Processing Systems (NeurIPS), 2023
Dongmin Park
Seola Choi
Doyoung Kim
Hwanjun Song
Jae-Gil Lee
NoLa
358
35
0
02 Nov 2023
LLaMA: Open and Efficient Foundation Language Models
LLaMA: Open and Efficient Foundation Language Models
Hugo Touvron
Thibaut Lavril
Gautier Izacard
Xavier Martinet
Marie-Anne Lachaux
...
Faisal Azhar
Aurelien Rodriguez
Armand Joulin
Edouard Grave
Guillaume Lample
ALMPILM
20.2K
19,547
0
27 Feb 2023
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial
  Examples for Supervised Learning Contribute the Least
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the LeastInternational Conference on Machine Learning (ICML), 2023
S. Joshi
Baharan Mirzasoleiman
SSL
600
29
0
18 Feb 2023
On The Computational Complexity of Self-Attention
On The Computational Complexity of Self-AttentionInternational Conference on Algorithmic Learning Theory (ALT), 2022
Feyza Duman Keles
Pruthuvi Maheshakya Wijewardena
Chinmay Hegde
398
262
0
11 Sep 2022
Beyond neural scaling laws: beating power law scaling via data pruning
Beyond neural scaling laws: beating power law scaling via data pruningNeural Information Processing Systems (NeurIPS), 2022
Ben Sorscher
Robert Geirhos
Shashank Shekhar
Surya Ganguli
Ari S. Morcos
2.0K
592
0
29 Jun 2022
Dataset Pruning: Reducing Training Data by Examining Generalization
  Influence
Dataset Pruning: Reducing Training Data by Examining Generalization InfluenceInternational Conference on Learning Representations (ICLR), 2022
Shuo Yang
Zeke Xie
Hanyu Peng
Minjing Xu
Mingming Sun
P. Li
DD
666
166
0
19 May 2022
Selective-Supervised Contrastive Learning with Noisy Labels
Selective-Supervised Contrastive Learning with Noisy LabelsComputer Vision and Pattern Recognition (CVPR), 2022
Shikun Li
Xiaobo Xia
Shiming Ge
Tongliang Liu
NoLa
415
234
0
08 Mar 2022
Vision Transformer for Small-Size Datasets
Vision Transformer for Small-Size Datasets
Seung Hoon Lee
Seunghyun Lee
B. Song
ViT
382
297
0
27 Dec 2021
Generalized Kernel Thinning
Generalized Kernel Thinning
Raaz Dwivedi
Lester W. Mackey
550
36
0
04 Oct 2021
Deep Learning on a Data Diet: Finding Important Examples Early in
  Training
Deep Learning on a Data Diet: Finding Important Examples Early in TrainingNeural Information Processing Systems (NeurIPS), 2021
Mansheej Paul
Surya Ganguli
Gintare Karolina Dziugaite
593
664
0
15 Jul 2021
Robust Training in High Dimensions via Block Coordinate Geometric Median
  Descent
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent
Anish Acharya
Abolfazl Hashemi
Prateek Jain
Sujay Sanghavi
Inderjit S. Dhillon
Ufuk Topcu
200
36
0
16 Jun 2021
Learning Transferable Visual Models From Natural Language Supervision
Learning Transferable Visual Models From Natural Language SupervisionInternational Conference on Machine Learning (ICML), 2021
Alec Radford
Jong Wook Kim
Chris Hallacy
Aditya A. Ramesh
Gabriel Goh
...
Amanda Askell
Pamela Mishkin
Jack Clark
Gretchen Krueger
Ilya Sutskever
CLIPVLM
2.2K
47,325
0
26 Feb 2021
An Image is Worth 16x16 Words: Transformers for Image Recognition at
  Scale
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Alexey Dosovitskiy
Lucas Beyer
Alexander Kolesnikov
Dirk Weissenborn
Xiaohua Zhai
...
Matthias Minderer
G. Heigold
Sylvain Gelly
Jakob Uszkoreit
N. Houlsby
ViT
1.6K
60,663
0
22 Oct 2020
What Neural Networks Memorize and Why: Discovering the Long Tail via
  Influence Estimation
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence EstimationNeural Information Processing Systems (NeurIPS), 2020
Vitaly Feldman
Chiyuan Zhang
TDI
725
610
0
09 Aug 2020
Denoising Diffusion Probabilistic Models
Denoising Diffusion Probabilistic Models
Jonathan Ho
Ajay Jain
Pieter Abbeel
DiffM
6.2K
29,328
0
19 Jun 2020
Language Models are Few-Shot Learners
Language Models are Few-Shot LearnersNeural Information Processing Systems (NeurIPS), 2020
Tom B. Brown
Benjamin Mann
Nick Ryder
Melanie Subbiah
Jared Kaplan
...
Christopher Berner
Sam McCandlish
Alec Radford
Ilya Sutskever
Dario Amodei
BDL
2.4K
57,120
0
28 May 2020
A Theory of Usable Information Under Computational Constraints
A Theory of Usable Information Under Computational ConstraintsInternational Conference on Learning Representations (ICLR), 2020
Yilun Xu
Shengjia Zhao
Jiaming Song
Russell Stewart
Stefano Ermon
290
210
0
25 Feb 2020
A Simple Framework for Contrastive Learning of Visual Representations
A Simple Framework for Contrastive Learning of Visual RepresentationsInternational Conference on Machine Learning (ICML), 2020
Ting-Li Chen
Simon Kornblith
Mohammad Norouzi
Geoffrey E. Hinton
SSL
1.5K
23,910
0
13 Feb 2020
Identifying Mislabeled Data using the Area Under the Margin Ranking
Identifying Mislabeled Data using the Area Under the Margin RankingNeural Information Processing Systems (NeurIPS), 2020
Geoff Pleiss
Tianyi Zhang
Ethan R. Elenberg
Kilian Q. Weinberger
NoLa
758
353
0
28 Jan 2020
Scaling Laws for Neural Language Models
Scaling Laws for Neural Language Models
Jared Kaplan
Sam McCandlish
T. Henighan
Tom B. Brown
B. Chess
R. Child
Scott Gray
Alec Radford
Jeff Wu
Dario Amodei
2.3K
7,549
0
23 Jan 2020
Choosing the Sample with Lowest Loss makes SGD Robust
Choosing the Sample with Lowest Loss makes SGD RobustInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Vatsal Shah
Xiaoxia Wu
Sujay Sanghavi
395
50
0
10 Jan 2020
Federated Variance-Reduced Stochastic Gradient Descent with Robustness
  to Byzantine Attacks
Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine AttacksIEEE Transactions on Signal Processing (IEEE Trans. Signal Process.), 2019
Zhaoxian Wu
Qing Ling
Tianyi Chen
G. Giannakis
FedMLAAML
260
229
0
29 Dec 2019
Recent Advances in Algorithmic High-Dimensional Robust Statistics
Recent Advances in Algorithmic High-Dimensional Robust Statistics
Ilias Diakonikolas
D. Kane
OOD
367
192
0
14 Nov 2019
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural NetworksInternational Conference on Machine Learning (ICML), 2019
Mingxing Tan
Quoc V. Le
3DVMedIm
907
23,288
0
28 May 2019
Benchmarking Neural Network Robustness to Common Corruptions and
  Perturbations
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Dan Hendrycks
Thomas G. Dietterich
OODVLM
3.0K
4,267
0
28 Mar 2019
An Empirical Study of Example Forgetting during Deep Neural Network
  Learning
An Empirical Study of Example Forgetting during Deep Neural Network Learning
Mariya Toneva
Alessandro Sordoni
Rémi Tachet des Combes
Adam Trischler
Yoshua Bengio
Geoffrey J. Gordon
903
943
0
12 Dec 2018
RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed
  Learning from Heterogeneous Datasets
RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous DatasetsAAAI Conference on Artificial Intelligence (AAAI), 2018
Liping Li
Canran Xu
Xiangnan He
Yixin Cao
Tat-Seng Chua
FedML
399
650
0
09 Nov 2018
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture
  Design
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Ningning Ma
Xiangyu Zhang
Haitao Zheng
Jian Sun
656
6,207
0
30 Jul 2018
Generalized Cross Entropy Loss for Training Deep Neural Networks with
  Noisy Labels
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
Zhilu Zhang
M. Sabuncu
NoLa
1.9K
3,122
0
20 May 2018
Not All Samples Are Created Equal: Deep Learning with Importance
  Sampling
Not All Samples Are Created Equal: Deep Learning with Importance SamplingInternational Conference on Machine Learning (ICML), 2018
Angelos Katharopoulos
François Fleuret
582
638
0
02 Mar 2018
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
Trevor Campbell
Tamara Broderick
376
146
0
05 Feb 2018
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks
  on Corrupted Labels
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Lu Jiang
Zhengyuan Zhou
Thomas Leung
Li Li
Li Fei-Fei
NoLa
704
1,644
0
14 Dec 2017
Deep Learning Scaling is Predictable, Empirically
Deep Learning Scaling is Predictable, Empirically
Joel Hestness
Sharan Narang
Newsha Ardalani
G. Diamos
Heewoo Jun
Hassan Kianinejad
Md. Mostofa Ali Patwary
Yang Yang
Yanqi Zhou
608
950
0
01 Dec 2017
Squeeze-and-Excitation Networks
Squeeze-and-Excitation Networks
Jie Hu
Li Shen
Samuel Albanie
Gang Sun
Enhua Wu
4.5K
33,354
0
05 Sep 2017
Towards Deep Learning Models Resistant to Adversarial Attacks
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry
Aleksandar Makelov
Ludwig Schmidt
Dimitris Tsipras
Adrian Vladu
SILMOOD
2.2K
14,491
0
19 Jun 2017
Understanding Black-box Predictions via Influence Functions
Understanding Black-box Predictions via Influence Functions
Pang Wei Koh
Abigail Z. Jacobs
TDI
774
3,472
0
14 Mar 2017
Making Deep Neural Networks Robust to Label Noise: a Loss Correction
  Approach
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
Giorgio Patrini
A. Rozza
A. Menon
Richard Nock
Zhuang Li
NoLa
558
1,649
0
13 Sep 2016
Robust Estimators in High Dimensions without the Computational
  Intractability
Robust Estimators in High Dimensions without the Computational Intractability
Ilias Diakonikolas
Gautam Kamath
D. Kane
Haibin Zhang
Ankur Moitra
Alistair Stewart
513
548
0
21 Apr 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
4.2K
226,071
0
10 Dec 2015
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial ExamplesInternational Conference on Learning Representations (ICLR), 2014
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAMLGAN
2.0K
21,884
0
20 Dec 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image RecognitionInternational Conference on Learning Representations (ICLR), 2014
Karen Simonyan
Andrew Zisserman
FAttMDE
4.0K
110,590
0
04 Sep 2014
Intriguing properties of neural networks
Intriguing properties of neural networksInternational Conference on Learning Representations (ICLR), 2013
Christian Szegedy
Wojciech Zaremba
Ilya Sutskever
Joan Bruna
D. Erhan
Ian Goodfellow
Rob Fergus
AAML
1.4K
16,393
1
21 Dec 2013
Stochastic Gradient Descent, Weighted Sampling, and the Randomized
  Kaczmarz algorithm
Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithmMathematical programming (Math. Program.), 2013
Deanna Needell
Nathan Srebro
Rachel A. Ward
593
598
0
21 Oct 2013
Geometric median and robust estimation in Banach spaces
Geometric median and robust estimation in Banach spaces
Stanislav Minsker
831
342
0
06 Aug 2013
On the Equivalence between Herding and Conditional Gradient Algorithms
On the Equivalence between Herding and Conditional Gradient AlgorithmsInternational Conference on Machine Learning (ICML), 2012
Francis R. Bach
Damien Scieur
G. Obozinski
442
176
0
20 Mar 2012
Super-Samples from Kernel Herding
Super-Samples from Kernel HerdingConference on Uncertainty in Artificial Intelligence (UAI), 2010
Yutian Chen
Max Welling
Alex Smola
730
386
0
15 Mar 2012
A Unified Framework for Approximating and Clustering Data
A Unified Framework for Approximating and Clustering DataSymposium on the Theory of Computing (STOC), 2011
Dan Feldman
M. Langberg
637
487
0
07 Jun 2011
Hilbert space embeddings and metrics on probability measures
Hilbert space embeddings and metrics on probability measuresJournal of machine learning research (JMLR), 2009
Bharath K. Sriperumbudur
Arthur Gretton
Kenji Fukumizu
Bernhard Schölkopf
Gert R. G. Lanckriet
730
820
0
30 Jul 2009
1
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