Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks

8 June 2019

Matthias Hein

Papers citing "Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks"

22 / 22 papers shown

Title
Interpretable Differencing of Machine Learning Models Swagatam Haldar Diptikalyan Saha Dennis L. Wei Rahul Nair Elizabeth M. Daly 16 1 0 10 Jun 2023
Adversarial Robustness for Tabular Data through Cost and Utility Awareness Klim Kireev B. Kulynych Carmela Troncoso AAML 26 16 0 27 Aug 2022
A Scalable, Interpretable, Verifiable & Differentiable Logic Gate Convolutional Neural Network Architecture From Truth Tables Adrien Benamira Tristan Guérand Thomas Peyrin Trevor Yap Bryan Hooi 40 1 0 18 Aug 2022
Provably Adversarially Robust Nearest Prototype Classifiers Václav Voráček Matthias Hein AAML 20 11 0 14 Jul 2022
Integrity Authentication in Tree Models Weijie Zhao Yingjie Lao Ping Li 59 5 0 30 May 2022
(De-)Randomized Smoothing for Decision Stump Ensembles Miklós Z. Horváth Mark Niklas Muller Marc Fischer Martin Vechev 30 3 0 27 May 2022
Beyond Robustness: Resilience Verification of Tree-Based Classifiers Stefano Calzavara Lorenzo Cazzaro Claudio Lucchese Federico Marcuzzi S. Orlando AAML 43 9 0 05 Dec 2021
Robustness of Graph Neural Networks at Scale Simon Geisler Tobias Schmidt Hakan cSirin Daniel Zügner Aleksandar Bojchevski Stephan Günnemann AAML 30 125 0 26 Oct 2021
Certifying Robustness to Programmable Data Bias in Decision Trees Anna P. Meyer Aws Albarghouthi Loris Dántoni 27 21 0 08 Oct 2021
Being Properly Improper Tyler Sypherd Richard Nock Lalitha Sankar FaML 39 10 0 18 Jun 2021
A Review of Formal Methods applied to Machine Learning Caterina Urban Antoine Miné 39 55 0 06 Apr 2021
SoK: Privacy-Preserving Collaborative Tree-based Model Learning Sylvain Chatel Apostolos Pyrgelis J. Troncoso-Pastoriza Jean-Pierre Hubaux 17 14 0 16 Mar 2021
T-Miner: A Generative Approach to Defend Against Trojan Attacks on DNN-based Text Classification A. Azizi I. A. Tahmid Asim Waheed Neal Mangaokar Jiameng Pu M. Javed Chandan K. Reddy Bimal Viswanath AAML 25 77 0 07 Mar 2021
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness Jacob D. Abernethy Pranjal Awasthi Satyen Kale AAML 27 6 0 01 Mar 2021
Connecting Interpretability and Robustness in Decision Trees through Separation Michal Moshkovitz Yao-Yuan Yang Kamalika Chaudhuri 33 22 0 14 Feb 2021
Efficient Training of Robust Decision Trees Against Adversarial Examples D. Vos S. Verwer AAML 6 36 0 18 Dec 2020
Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examples Yael Mathov Eden Levy Ziv Katzir A. Shabtai Yuval Elovici AAML 31 14 0 07 Oct 2020
SoK: Certified Robustness for Deep Neural Networks Linyi Li Tao Xie Bo-wen Li AAML 33 128 0 09 Sep 2020
When are Non-Parametric Methods Robust? Robi Bhattacharjee Kamalika Chaudhuri AAML 44 28 0 13 Mar 2020
Robustness for Non-Parametric Classification: A Generic Attack and Defense Yao-Yuan Yang Cyrus Rashtchian Yizhen Wang Kamalika Chaudhuri SILM AAML 34 42 0 07 Jun 2019
Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges Rob Ashmore R. Calinescu Colin Paterson AI4TS 27 116 0 10 May 2019
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 249 1,842 0 03 Feb 2017