Fetching the paper…
Reading the bibliography…
As the use of machine learning (ML) models is becoming increasingly popular in many real-world applications, there are practical challenges that need to be addressed for model maintenance.
Making deep neural networks robust to label noise: a loss correction approach. In Proc. CVPR . 1944–1952
G. Patrini, A. Rozza, A. Krishna Menon, R. Nock, and L. Qu. 2017 · 1952
Earlier work this paper cites.
Equation of state calculations by fast computing machines
Nicholas Metropolis, Arianna W Rosenbluth, Marshall N Rosenbluth, Augusta H Teller, and Edward Teller. 1953 · 1953
Earlier work this paper cites.
Monte Carlo sampling methods using Markov chains and their applications
W Keith Hastings. 1970 · 1970
Earlier work this paper cites.
Residuals and influence in regression
R Dennis Cook and Sanford Weisberg. 1982 · 1982
Earlier work this paper cites.
Learning from noisy examples
Dana Angluin and Philip Laird. 1988 · 1988
Earlier work this paper cites.
Efficient noise-tolerant learning from statistical queries
Michael Kearns. 1998 · 1998
Earlier work this paper cites.
Evaluating the Wisdom of Crowds in Assessing Phishing Websites. In Financial Cryptography and Data Security . 16–30
Tyler Moore and Richard Clayton. 2008 · 2008
Earlier work this paper cites.
Graph-based malware detection using dynamic analysis
Blake Anderson, Daniel Quist, Joshua Neil, Curtis Storlie, and Terran Lane. 2011 · 2011
Earlier work this paper cites.
Handbook of Markov chain Monte Carlo
Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng. 2011 · 2011
Earlier work this paper cites.
MCMC using Hamiltonian dynamics
Radford M Neal et al · 2011
Earlier work this paper cites.
Bayesian learning via stochastic gradient Langevin dynamics. In Proc. ICML . 681–688
Max Welling and Yee W Teh. 2011 · 2011
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2013 · 2013
Earlier work this paper cites.
Stochastic gradient Hamiltonian Monte Carlo. In Proc. ICML . 1683–1691
Tianqi Chen, Emily Fox, and Carlos Guestrin. 2014 · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014 · 2014
Earlier work this paper cites.
The no-u-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
Matthew D Hoffman, Andrew Gelman, et al · 2014
Earlier work this paper cites.
Towards making systems forget with machine unlearning. In Proc. IEEE Symposium on Security and Privacy . 463–480
Yinzhi Cao and Junfeng Yang. 2015 · 2015
Earlier work this paper cites.
Deep neural network based malware detection using two dimensional binary program features. In Proc. 10th International Conference on Malicious and Unwanted Software (MALWARE) . 11–20
Joshua Saxe and Konstantin Berlin. 2015 · 2015
Earlier work this paper cites.
Identifying Encrypted Malware Traffic with Contextual Flow Data. In Proc. ACM Workshop on Artificial Intelligence and Security (AISec ’16) . 35–46
Blake Anderson and David McGrew. 2016 · 2016
Earlier work this paper cites.
Everything you need to know about the “Right to be forgotten”
GDPR.EU. 2016 · 2016
Earlier work this paper cites.
Know your phish: Novel techniques for detecting phishing sites and their targets. In Proc. IEEE 36th International Conference on Distributed Computing Systems (ICDCS) . 323–333
Samuel Marchal, Kalle Saari, Nidhi Singh, and N Asokan. 2016 · 2016
Earlier work this paper cites.
Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling. In In Proc. NeurIPS , Vol. 30. Curran Associates, Inc
Andrei-Cristian Barbos, Francois Caron, Jean-François Giovannelli, and Arnaud Doucet. 2017 · 2017
Cited alongside, same era.
A conceptual introduction to Hamiltonian Monte Carlo
Michael Betancourt. 2017 · 2017
Cited alongside, same era.
UCI Machine Learning Repository
Dheeru Dua and Casey Graff. 2017 · 2017
Cited alongside, same era.
Training deep neural-networks using a noise adaptation layer. In Proc. ICLR
Jacob Goldberger and Ehud Ben-Reuven. 2017 · 2017
Cited alongside, same era.
Understanding black-box predictions via influence functions. In Proc. ICML . 1885–1894
Pang Wei Koh and Percy Liang. 2017 · 2017
Cited alongside, same era.
An Analysis of Phishing Blacklists: Google Safe Browsing, OpenPhish, and PhishTank. In Proc. Australasian Computer Science Week Multiconference
Simon Bell and Peter Komisarczuk. 2020 · 2020
Later among the works it cites.
Scaling Hamiltonian Monte Carlo inference for Bayesian neural networks with symmetric splitting
Adam D Cobb and Brian Jalaian. 2020 · 2020
Later among the works it cites.
Formalizing Data Deletion in the Context of the Right to be Forgotten
Sanjam Garg, Shafi Goldwasser, and Prashant Nalini Vasudevan. 2020 · 2020
Later among the works it cites.
Building robust phishing detection system: an empirical analysis. In NDSS Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb)
Jehyun Lee, Pingxiao Ye, Ruofan Liu, Dinil Mon Divakaran, and Mun Choon Chan. 2020 · 2020
Later among the works it cites.
Variational Bayesian unlearning. In Proc. NeurIPS
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018 · 2018
Cited alongside, same era.
EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models
Hyrum S. Anderson and Phil Roth. 2018 · 2018
Cited alongside, same era.
URLNet: Learning a URL representation with deep learning for malicious URL detection
Hung Le, Quang Pham, Doyen Sahoo, and Steven CH Hoi. 2018 · 2018
Cited alongside, same era.
Anomaly Detection and Attribution in Networks With Temporally Correlated Traffic
Ido Nevat, Dinil Mon Divakaran, Sai Ganesh Nagarajan, Pengfei Zhang, Su Le, Ko Li Ling, and Vrizlynn Thing. 2018 · 2018
Cited alongside, same era.
Accelerating MCMC algorithms
Christian P Robert, Víctor Elvira, Nick Tawn, and Changye Wu. 2018 · 2018
Cited alongside, same era.
Humans forget, machines remember: Artificial intelligence and the Right to Be Forgotten
Eduard Fosch Villaronga, Peter Kieseberg, and Tiffany Li. 2018 · 2018
Cited alongside, same era.
Phishing webpage detection dataset
2019 · 2019
Cited alongside, same era.
Quoc Phong Nguyen, Bryan Kian Hsiang Low, and Patrick Jaillet. 2020 · 2020
Later among the works it cites.
Sunrise to Sunset: Analyzing the End-to-end Life Cycle and Effectiveness of Phishing Attacks at Scale. In 29th USENIX Security Symposium . 361–377
Adam Oest, Penghui Zhang, Brad Wardman, Eric Nunes, Jakub Burgis, Ali Zand, Kurt Thomas, Adam Doupé, and Gail-Joon Ahn. 2020 · 2020
Later among the works it cites.
Cyclical stochastic gradient MCMC for Bayesian deep learning. In Proc. ICLR
Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, and Andrew Gordon Wilson. 2020 · 2020
Later among the works it cites.
Alexa top rank websites
[Accessed: Nov. 2021] · 2021
Later among the works it cites.
Anomali
[Accessed: Nov. 2021] · 2021
Later among the works it cites.
OpenPhish
[Accessed: Nov. 2021] · 2021
Later among the works it cites.
PhishTank
[Accessed: Nov. 2021] · 2021
Later among the works it cites.
URLhaus
[Accessed: Nov. 2021] · 2021
Later among the works it cites.
Shaopeng Fu, Fengxiang He, and Dacheng Tao. 2021 · 2021
Later among the works it cites.
D-Fence: A Flexible, Efficient, and Comprehensive Phishing Email Detection System. In IEEE European Symposium on Security and Privacy (IEEE EuroS&P) . 578–597
Jehyun Lee, Farren Tang, Pingxiao Ye, Fahim Abbasi, Phil Hay, and Dinil Mon Divakaran. 2021 · 2021
Later among the works it cites.
Phishpedia: A Hybrid Deep Learning Based Approach to Visually Identify Phishing Webpages. In 30th USENIX Security Symposium
Yun Lin, Ruofan Liu, Dinil Mon Divakaran, Jun Yang Ng, Qing Zhou Chan, Yiwen Lu, Yuxuan Si, Fan Zhang, and Jin Song Dong. 2021 · 2021
Later among the works it cites.
Most Phishing Attacks Use Compromised Domains and Free Hosting
PHISHLABS. 2021 · 2021
Later among the works it cites.
Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers. In 30th USENIX Security Symposium
Giorgio Severi, Jim Meyer, Scott Coull, and Alina Oprea. 2021 · 2021
Later among the works it cites.
Near-Optimal Task Selection for Meta-Learning with Mutual Information and Online Variational Bayesian Unlearning. In Proc. AISTATS
Yizhou Chen, Shizhuo Zhang, and Bryan Kian Hsiang Low. 2022 · 2022
Closest in time.
Knowledge removal in sampling-based Bayesian inference. In Proc. ICLR
Shaopeng Fu, Fengxiang He, and Dacheng Tao. 2022 · 2022
Closest in time.