Improving Calibration through the Relationship with Adversarial
Robustness

Improving Calibration through the Relationship with Adversarial Robustness

29 June 2020

Papers citing "Improving Calibration through the Relationship with Adversarial Robustness"

12 / 12 papers shown

Title
Towards Certification of Uncertainty Calibration under Adversarial Attacks Cornelius Emde Francesco Pinto Thomas Lukasiewicz Philip Torr Adel Bibi AAML 45 0 0 22 May 2024
Robust Survival Analysis with Adversarial Regularization Michael Potter Stefano Maxenti Michael Everett AAML 24 0 0 26 Dec 2023
Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method Yukun Zhao Lingyong Yan Weiwei Sun Guoliang Xing Chong Meng Shuaiqiang Wang Zhicong Cheng Zhaochun Ren Dawei Yin 29 35 0 27 Oct 2023
Mitigating Adversarial Vulnerability through Causal Parameter Estimation by Adversarial Double Machine Learning Byung-Kwan Lee Junho Kim Yonghyun Ro AAML 18 9 0 14 Jul 2023
Calibration Meets Explanation: A Simple and Effective Approach for Model Confidence Estimates Dongfang Li Baotian Hu Qingcai Chen 16 8 0 06 Nov 2022
Robust Models are less Over-Confident Julia Grabinski Paul Gavrikov J. Keuper M. Keuper AAML 36 24 0 12 Oct 2022
Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis Yuxin Xiao Paul Pu Liang Umang Bhatt W. Neiswanger Ruslan Salakhutdinov Louis-Philippe Morency 175 86 0 10 Oct 2022
Neural Clamping: Joint Input Perturbation and Temperature Scaling for Neural Network Calibration Yu Tang Pin-Yu Chen Tsung-Yi Ho 23 5 0 23 Sep 2022
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks Sizhe Chen Zhehao Huang Qinghua Tao Yingwen Wu Cihang Xie X. Huang AAML 110 28 0 24 May 2022
Are Vision Transformers Robust to Patch Perturbations? Jindong Gu Volker Tresp Yao Qin AAML ViT 38 60 0 20 Nov 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,661 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015