ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2003.01908
  4. Cited By
Denoised Smoothing: A Provable Defense for Pretrained Classifiers
v1v2 (latest)

Denoised Smoothing: A Provable Defense for Pretrained Classifiers

4 March 2020
Hadi Salman
Mingjie Sun
Greg Yang
Ashish Kapoor
J. Zico Kolter
ArXiv (abs)PDFHTMLGithub (97★)

Papers citing "Denoised Smoothing: A Provable Defense for Pretrained Classifiers"

16 / 16 papers shown
CR-UTP: Certified Robustness against Universal Text Perturbations on
  Large Language Models
CR-UTP: Certified Robustness against Universal Text Perturbations on Large Language Models
Qian Lou
Xin Liang
Jiaqi Xue
Yancheng Zhang
Rui Xie
Mengxin Zheng
AAML
317
0
0
04 Jun 2024
DiffAug: A Diffuse-and-Denoise Augmentation for Training Robust
  Classifiers
DiffAug: A Diffuse-and-Denoise Augmentation for Training Robust ClassifiersNeural Information Processing Systems (NeurIPS), 2023
Chandramouli Shama Sastry
Sri Harsha Dumpala
Sageev Oore
319
4
0
15 Jun 2023
Understanding Noise-Augmented Training for Randomized Smoothing
Understanding Noise-Augmented Training for Randomized Smoothing
Ambar Pal
Jeremias Sulam
AAML
460
8
0
08 May 2023
Better May Not Be Fairer: A Study on Subgroup Discrepancy in Image
  Classification
Better May Not Be Fairer: A Study on Subgroup Discrepancy in Image ClassificationIEEE International Conference on Computer Vision (ICCV), 2022
Ming-Chang Chiu
Pin-Yu Chen
Xuezhe Ma
291
9
0
16 Dec 2022
Learning Representations Robust to Group Shifts and Adversarial Examples
Learning Representations Robust to Group Shifts and Adversarial Examples
Ming-Chang Chiu
Xuezhe Ma
OOD
145
0
0
18 Feb 2022
On the Certified Robustness for Ensemble Models and Beyond
On the Certified Robustness for Ensemble Models and BeyondInternational Conference on Learning Representations (ICLR), 2021
Zhuolin Yang
Linyi Li
Xiaojun Xu
B. Kailkhura
Tao Xie
Yue Liu
AAML
459
55
0
22 Jul 2021
Data-Dependent Randomized Smoothing
Data-Dependent Randomized Smoothing
Motasem Alfarra
Adel Bibi
Juil Sock
Guohao Li
UQCV
451
41
0
08 Dec 2020
Almost Tight L0-norm Certified Robustness of Top-k Predictions against
  Adversarial Perturbations
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial PerturbationsInternational Conference on Learning Representations (ICLR), 2020
Jinyuan Jia
Binghui Wang
Xiaoyu Cao
Hongbin Liu
Neil Zhenqiang Gong
327
26
0
15 Nov 2020
Adversarial Robustness of Supervised Sparse Coding
Adversarial Robustness of Supervised Sparse Coding
Jeremias Sulam
Ramchandran Muthumukar
R. Arora
AAML
343
26
0
22 Oct 2020
Reducing Adversarially Robust Learning to Non-Robust PAC Learning
Reducing Adversarially Robust Learning to Non-Robust PAC Learning
Omar Montasser
Steve Hanneke
Nathan Srebro
339
33
0
22 Oct 2020
Semantically Adversarial Learnable Filters
Semantically Adversarial Learnable Filters
Ali Shahin Shamsabadi
Changjae Oh
Andrea Cavallaro
GAN
397
8
0
13 Aug 2020
Learning perturbation sets for robust machine learning
Learning perturbation sets for robust machine learningInternational Conference on Learning Representations (ICLR), 2020
Eric Wong
J. Zico Kolter
OOD
272
85
0
16 Jul 2020
Adversarial robustness via robust low rank representations
Adversarial robustness via robust low rank representationsNeural Information Processing Systems (NeurIPS), 2020
Pranjal Awasthi
Himanshu Jain
A. S. Rawat
Aravindan Vijayaraghavan
AAML
273
25
0
13 Jul 2020
Certifying Joint Adversarial Robustness for Model Ensembles
Certifying Joint Adversarial Robustness for Model Ensembles
M. Jonas
David Evans
AAML
175
2
0
21 Apr 2020
A Convex Parameterization of Robust Recurrent Neural Networks
A Convex Parameterization of Robust Recurrent Neural Networks
Max Revay
Ruigang Wang
I. Manchester
353
4
0
11 Apr 2020
Randomized Smoothing of All Shapes and Sizes
Randomized Smoothing of All Shapes and SizesInternational Conference on Machine Learning (ICML), 2020
Greg Yang
Tony Duan
J. E. Hu
Hadi Salman
Ilya P. Razenshteyn
Jungshian Li
AAML
581
234
0
19 Feb 2020
1
Page 1 of 1