Fetching the paper…
Reading the bibliography…
The machine learning community is increasingly recognizing the importance of fostering trust and safety in modern generative AI (GenAI) models.
Reverse-time diffusion equation models
Brian D.O. Anderson · 1982
Earlier work this paper cites.
An empirical Bayes approach to statistics
Herbert E Robbins · 1992
Earlier work this paper cites.
Exact penalization and necessary optimality conditions for generalized bilevel programming problems
JJ Ye, DL Zhu, and Qiji Jim Zhu · 1997
Earlier work this paper cites.
Adversarial learning
Daniel Lowd and Christopher Meek · 2005
Earlier work this paper cites.
Differential privacy
Cynthia Dwork · 2006
Earlier work this paper cites.
Robust de-anonymization of large sparse datasets
Arvind Narayanan and Vitaly Shmatikov · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
Earlier work this paper cites.
Tweedie’s formula and selection bias
Bradley Efron · 2011
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
Earlier work this paper cites.
Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Improved techniques for training GANs
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Earlier work this paper cites.
GANs trained by a two time-scale update rule converge to a local Nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Earlier work this paper cites.
Semi-implicit variational inference
Mingzhang Yin and Mingyuan Zhou · 2018
Earlier work this paper cites.
Making ai forget you: Data deletion in machine learning
Antonio Ginart, Melody Guan, Gregory Valiant, and James Y Zou · 2019
Earlier work this paper cites.
Certified data removal from machine learning models
Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens Van Der Maaten · 2019
Earlier work this paper cites.
The european union general data protection regulation: what it is and what it means
Chris Jay Hoofnagle, Bart Van Der Sloot, and Frederik Zuiderveen Borgesius · 2019
Earlier work this paper cites.
Improved precision and recall metric for assessing generative models
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2019
Earlier work this paper cites.
Generative Modeling by Estimating Gradients of the Data Distribution
Yang Song and Stefano Ermon · 2019
Earlier work this paper cites.
Eternal sunshine of the spotless net: Selective forgetting in deep networks
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
Earlier work this paper cites.
Denoising Diffusion Probabilistic Models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
Earlier work this paper cites.
Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel Johnson, Jonathan Ho, Danny Tarlow, and Rianne van den Berg · 2021
Earlier work this paper cites.
Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021
Earlier work this paper cites.
Diffusion models beat GANs on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2021
Cited alongside, same era.
Argmax flows and multinomial diffusion: Learning categorical distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling · 2021
Cited alongside, same era.
Approximate data deletion from machine learning models
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, and James Zou · 2021
Cited alongside, same era.
Descent-to-delete: Gradient-based methods for machine unlearning
Seth Neel, Aaron Roth, and Saeed Sharifi-Malvajerdi · 2021
Cited alongside, same era.
GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models
Fast federated machine unlearning with nonlinear functional theory
Tianshi Che, Yang Zhou, Zijie Zhang, Lingjuan Lyu, Ji Liu, Da Yan, Dejing Dou, and Jun Huan · 2023
Later among the works it cites.
Learning to Jump: Thinning and thickening latent counts for generative modeling
Tianqi Chen and Mingyuan Zhou · 2023
Later among the works it cites.
Diffusion posterior sampling for general noisy inverse problems
Hyungjin Chung, Jeongsol Kim, Michael Thompson Mccann, Marc Louis Klasky, and Jong Chul Ye · 2023
Later among the works it cites.
Erasing concepts from diffusion models
Rohit Gandikota, Joanna Materzynska, Jaden Fiotto-Kaufman, and David Bau · 2023
Later among the works it cites.
A two-timescale stochastic algorithm framework for bilevel optimization: Complexity analysis and application to actor-critic
Mingyi Hong, Hoi-To Wai, Zhaoran Wang, and Zhuoran Yang · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
Cited alongside, same era.
Remember what you want to forget: Algorithms for machine unlearning
Ayush Sekhari, Jayadev Acharya, Gautam Kamath, and Ananda Theertha Suresh · 2021
Cited alongside, same era.
Systematic evaluation of privacy risks of machine learning models
Liwei Song and Prateek Mittal · 2021
Cited alongside, same era.
A prototype-oriented framework for unsupervised domain adaptation
Korawat Tanwisuth, Xinjie Fan, Huangjie Zheng, Shujian Zhang, Hao Zhang, Bo Chen, and Mingyuan Zhou · 2021
Cited alongside, same era.
Machine unlearning of features and labels
Alexander Warnecke, Lukas Pirch, Christian Wressnegger, and Konrad Rieck · 2021
Cited alongside, same era.
Tackling the generative learning trilemma with denoising diffusion gans
Zhisheng Xiao, Karsten Kreis, and Arash Vahdat · 2021
Cited alongside, same era.
Alignment attention by matching key and query distributions
Shujian Zhang, Xinjie Fan, Huangjie Zheng, Korawat Tanwisuth, and Mingyuan Zhou · 2021
Cited alongside, same era.
A data-based perspective on transfer learning
Saachi Jain, Hadi Salman, Alaa Khaddaj, Eric Wong, Sung Min Park, and Aleksander Mądry · 2023
Later among the works it cites.
Model sparsification can simplify machine unlearning
Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, and Sijia Liu · 2023
Later among the works it cites.
Ablating concepts in text-to-image diffusion models
Nupur Kumari, Bingliang Zhang, Sheng-Yu Wang, Eli Shechtman, Richard Zhang, and Jun-Yan Zhu · 2023
Later among the works it cites.
Diff-Instruct: A universal approach for transferring knowledge from pre-trained diffusion models
Weijian Luo, Tianyang Hu, Shifeng Zhang, Jiacheng Sun, Zhenguo Li, and Zhihua Zhang · 2023
Later among the works it cites.
SwiftBrush: One-step text-to-image diffusion model with variational score distillation
Thuan Hoang Nguyen and Anh Tran · 2023
Later among the works it cites.
DreamFusion: Text-to-3D using 2D diffusion
Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall · 2023
Later among the works it cites.
Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models
Patrick Schramowski, Manuel Brack, Björn Deiseroth, and Kristian Kersting · 2023
Later among the works it cites.
Glaze: Protecting artists from style mimicry by { \{ Text-to-Image } \} models
Shawn Shan, Jenna Cryan, Emily Wenger, Haitao Zheng, Rana Hanocka, and Ben Y Zhao · 2023
Later among the works it cites.
On penalty-based bilevel gradient descent method
Han Shen, Quan Xiao, and Tianyi Chen · 2023
Later among the works it cites.
Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
Later among the works it cites.
Pouf: Prompt-oriented unsupervised fine-tuning for large pre-trained models
Korawat Tanwisuth, Shujian Zhang, Huangjie Zheng, Pengcheng He, and Mingyuan Zhou · 2023
Later among the works it cites.
ProlificDreamer: High-fidelity and diverse text-to-3D generation with variational score distillation, 2023
Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu · 2023
Later among the works it cites.
Certified edge unlearning for graph neural networks
Kun Wu, Jie Shen, Yue Ning, Ting Wang, and Wendy Hui Wang · 2023
Later among the works it cites.
Ufogen: You forward once large scale text-to-image generation via diffusion gans
Yanwu Xu, Yang Zhao, Zhisheng Xiao, and Tingbo Hou · 2023
Later among the works it cites.
Yimeng Zhang, Jinghan Jia, Xin Chen, Aochuan Chen, Yihua Zhang, Jiancheng Liu, Ke Ding, and Sijia Liu · 2023
Later among the works it cites.
Mingyuan Zhou, Tianqi Chen, Zhendong Wang, and Huangjie Zheng · 2023
Later among the works it cites.
Salun: Empowering machine unlearning via gradient-based weight saliency in both image classification and generation
Chongyu Fan, Jiancheng Liu, Yihua Zhang, Eric Wong, Dennis Wei, and Sijia Liu · 2024
Closest in time.
Selective amnesia: A continual learning approach to forgetting in deep generative models
Alvin Heng and Harold Soh · 2024
Closest in time.
Model sparsity can simplify machine unlearning
Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, PRANAY SHARMA, Sijia Liu, et al · 2024
Closest in time.
Diff-instruct: A universal approach for transferring knowledge from pre-trained diffusion models
Weijian Luo, Tianyang Hu, Shifeng Zhang, Jiacheng Sun, Zhenguo Li, and Zhihua Zhang · 2024
Closest in time.
Fair machine unlearning: Data removal while mitigating disparities
Alex Oesterling, Jiaqi Ma, Flavio Calmon, and Himabindu Lakkaraju · 2024
Closest in time.
SDXL: Improving latent diffusion models for high-resolution image synthesis
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach · 2024
Closest in time.
One-step diffusion with distribution matching distillation
Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T Freeman, and Taesung Park · 2024
Closest in time.
Learning stackable and skippable LEGO bricks for efficient, reconfigurable, and variable-resolution diffusion modeling
Huangjie Zheng, Zhendong Wang, Jianbo Yuan, Guanghan Ning, Pengcheng He, Quanzeng You, Hongxia Yang, and Mingyuan Zhou · 2024
Closest in time.
To generate or not? safety-driven unlearned diffusion models are still easy to generate unsafe images… for now
Yimeng Zhang, Jinghan Jia, Xin Chen, Aochuan Chen, Yihua Zhang, Jiancheng Liu, Ke Ding, and Sijia Liu · 2025
Closest in time.