Fetching the paper…
Reading the bibliography…
We introduce a refined differentially private (DP) data structure for kernel density estimation (KDE), offering not only improved privacy-utility tradeoff but also better efficiency over prior results.
Lectures on discrete geometry
Jirí Matousek · 2002
Earlier work this paper cites.
Learning with kernels: support vector machines, regularization, optimization, and beyond
Bernhard Schölkopf and Alexander J Smola · 2002
Earlier work this paper cites.
Kernel methods for pattern analysis
John Shawe-Taylor and Nello Cristianini · 2004
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Kernel methods in machine learning
Thomas Hofmann, Bernhard Schölkopf, and Alexander Smola · 2007
Earlier work this paper cites.
Boosting and differential privacy
Cynthia Dwork, Guy N. Rothblum, and Salil Vadhan · 2010
Earlier work this paper cites.
Iterative constructions and private data release
Anupam Gupta, Aaron Roth, and Jonathan Ullman · 2012
Earlier work this paper cites.
A learning theory approach to noninteractive database privacy
Avrim Blum, Katrina Ligett, and Aaron Roth · 2013
Earlier work this paper cites.
Differential privacy for functions and functional data
Rob Hall, Alessandro Rinaldo, and Larry A. Wasserman · 2013
Earlier work this paper cites.
Exploiting metric structure for efficient private query release
Zhiyi Huang and Aaron Roth · 2014
Earlier work this paper cites.
Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
Earlier work this paper cites.
Differentially private data releasing for smooth queries
Ziteng Wang, Chi Jin, Kai Fan, Jiaqi Zhang, Junliang Huang, Yiqiao Zhong, and Liwei Wang · 2016
Earlier work this paper cites.
The bernstein mechanism: Function release under differential privacy
Francesco Aldà and Benjamin I. P. Rubinstein · 2017
Earlier work this paper cites.
Efficient density evaluation for smooth kernels
Arturs Backurs, Moses Charikar, Piotr Indyk, and Paris Siminelakis · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
Space and time efficient kernel density estimation in high dimensions
Arturs Backurs, Piotr Indyk, and Tal Wagner · 2019
Earlier work this paper cites.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 2019
Earlier work this paper cites.
Privacy-aware synthesizing for crowdsourced data
Mengdi Huai, Di Wang, Chenglin Miao, Jinhui Xu, and Aidong Zhang · 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.
Algorithms and hardness for linear algebra on geometric graphs
Josh Alman, Timothy Chu, Aaron Schild, and Zhao Song · 2020
Earlier work this paper cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
Kernel density estimation through density constrained near neighbor search
Moses Charikar, Mikhail Kapralov, Navid Nouri, and Paris Siminelakis · 2020
Earlier work this paper cites.
Gan-leaks: A taxonomy of membership inference attacks against generative models
Dingfan Chen, Ning Yu, Yang Zhang, and Mario Fritz · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
Using gans for sharing networked time series data: Challenges, initial promise, and open questions
Zinan Lin, Alankar Jain, Chen Wang, Giulia Fanti, and Vyas Sekar · 2020
Earlier work this paper cites.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
Earlier work this paper cites.
Privacy-preserving synthetic location data in the real world
Teddy Cunningham, Graham Cormode, and Hakan Ferhatosmanoğlu · 2021
Earlier work this paper cites.
A one-pass distributed and private sketch for kernel sums with applications to machine learning at scale
Benjamin Coleman and Anshumali Shrivastava · 2021
Earlier work this paper cites.
Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
Earlier work this paper cites.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
Earlier work this paper cites.
P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Lam Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang · 2021
Cited alongside, same era.
Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
Cited alongside, same era.
Ethical and social risks of harm from language models
Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, Atoosa Kasirzadeh, et al · 2021
Cited alongside, same era.
Do not let privacy overbill utility: Gradient embedding perturbation for private learning
Da Yu, Huishuai Zhang, Wei Chen, and Tie-Yan Liu · 2021
Cited alongside, same era.
Scalable and transferable black-box jailbreaks for language models via persona modulation
Rusheb Shah, Soroush Pour, Arush Tagade, Stephen Casper, Javier Rando, et al · 2023
Later among the works it cites.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
Later among the works it cites.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 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…
Optimal-degree polynomial approximations for exponentials and gaussian kernel density estimation
Amol Aggarwal and Josh Alman · 2022
Cited alongside, same era.
Sub-quadratic algorithms for kernel matrices via kernel density estimation, 2022
Ainesh Bakshi, Piotr Indyk, Praneeth Kacham, Sandeep Silwal, and Samson Zhou · 2022
Cited alongside, same era.
Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramèr · 2022
Cited alongside, same era.
Unlocking high-accuracy differentially private image classification through scale
Soham De, Leonard Berrada, Jamie Hayes, Samuel L. Smith, and Borja Balle · 2022
Cited alongside, same era.
Yichuan Deng, Wenyu Jin, Zhao Song, Xiaorui Sun, and Omri Weinstein · 2022
Cited alongside, same era.
Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models
Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al · 2022
Cited alongside, same era.
A dynamic low-rank fast gaussian transform
Baihe Huang, Zhao Song, Omri Weinstein, Junze Yin, Hengjie Zhang, and Ruizhe Zhang · 2022
Cited alongside, same era.
Reconstructing training data from trained neural networks
Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, and Michal Irani · 2022
Cited alongside, same era.
Fast private kernel density estimation via locality sensitive quantization
Tal Wagner, Yonatan Naamad, and Nina Mishra · 2023
Later among the works it cites.
Synthetic text generation with differential privacy: A simple and practical recipe
Xiang Yue, Huseyin A. Inan, Xuechen Li, Girish Kumar, Julia McAnallen, Hoda Shajari, Huan Sun, David Levitan, and Robert Sim · 2023
Later among the works it cites.
Gptfuzzer: Red teaming large language models with auto-generated jailbreak prompts
Jiahao Yu, Xingwei Lin, and Xinyu Xing · 2023
Later among the works it cites.
The claude 3 model family: Opus, sonnet, haiku, 2023
Anthropic · 2024
Closest in time.
How to capture higher-order correlations? generalizing matrix softmax attention to kronecker computation
Josh Alman and Zhao Song · 2024
Closest in time.
Efficiently computing similarities to private datasets
Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, and Jakub Tarnawski · 2024
Closest in time.
On computational limits of modern hopfield models: A fine-grained complexity analysis
Jerry Yao-Chieh Hu, Thomas Lin, Zhao Song, and Han Liu · 2024
Closest in time.
Poster: Identifying and mitigating vulnerabilities in llm-integrated applications
Fengqing Jiang, Zhangchen Xu, Luyao Niu, Boxin Wang, Jinyuan Jia, Bo Li, and Radha Poovendran · 2024
Closest in time.
Differentially private synthetic data via foundation model apis 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, and Sergey Yekhanin · 2024
Closest in time.
Shake to leak: Fine-tuning diffusion models can amplify the generative privacy risk
Zhangheng Li, Junyuan Hong, Bo Li, and Zhangyang Wang · 2024
Closest in time.
A tighter complexity analysis of sparsegpt
Xiaoyu Li, Yingyu Liang, Zhenmei Shi, and Zhao Song · 2024
Closest in time.
Decoupled alignment for robust plug-and-play adaptation
Haozheng Luo, Jiahao Yu, Wenxin Zhang, Jialong Li, Jerry Yao-Chieh Hu, Xingyu Xin, and Han Liu · 2024
Closest in time.
Sora: A review on background, technology, limitations, and opportunities of large vision models, 2024
Yixin Liu, Kai Zhang, Yuan Li, Zhiling Yan, Chujie Gao, Ruoxi Chen, Zhengqing Yuan, Yue Huang, Hanchi Sun, Jianfeng Gao, Lifang He, and Lichao Sun · 2024
Closest in time.
Ai risk management should incorporate both safety and security
Xiangyu Qi, Yangsibo Huang, Yi Zeng, Edoardo Debenedetti, Jonas Geiping, Luxi He, Kaixuan Huang, Udari Madhushani, Vikash Sehwag, Weijia Shi, et al · 2024
Closest in time.
“Do Anything Now”: Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models
Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang · 2024
Closest in time.
Trustllm: Trustworthiness in large language models
Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, et al · 2024
Closest in time.
Protein conformation generation via force-guided se (3) diffusion models
Yan Wang, Lihao Wang, Yuning Shen, Yiqun Wang, Huizhuo Yuan, Yue Wu, and Quanquan Gu · 2024
Closest in time.
Diffusion language models are versatile protein learners
Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, and Quanquan Gu · 2024
Closest in time.
Differentially private synthetic data via foundation model apis 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin A. Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, and Sergey Yekhanin · 2024
Closest in time.
Sorry-bench: Systematically evaluating large language model safety refusal behaviors
Tinghao Xie, Xiangyu Qi, Yi Zeng, Yangsibo Huang, Udari Madhushani Sehwag, Kaixuan Huang, Luxi He, Boyi Wei, Dacheng Li, Ying Sheng, et al · 2024
Closest in time.
Selective pre-training for private fine-tuning
Da Yu, Sivakanth Gopi, Janardhan Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, and Huishuai Zhang · 2024
Closest in time.
Enhancing jailbreak attack against large language models through silent tokens
Jiahao Yu, Haozheng Luo, Jerry Yao-Chieh Hu, Wenbo Guo, Han Liu, and Xinyu Xing · 2024
Closest in time.
Decompopt: Controllable and decomposed diffusion models for structure-based molecular optimization
Xiangxin Zhou, Xiwei Cheng, Yuwei Yang, Yu Bao, Liang Wang, and Quanquan Gu · 2024
Closest in time.
Antigen-specific antibody design via direct energy-based preference optimization
Xiangxin Zhou, Dongyu Xue, Ruizhe Chen, Zaixiang Zheng, Liang Wang, and Quanquan Gu · 2024
Closest in time.
Air-bench 2024: A safety benchmark based on risk categories from regulations and policies
Yi Zeng, Yu Yang, Andy Zhou, Jeffrey Ziwei Tan, Yuheng Tu, Yifan Mai, Kevin Klyman, Minzhou Pan, Ruoxi Jia, Dawn Song, et al · 2024
Closest in time.
Llamafactory: Unified efficient fine-tuning of 100+ language models
Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, and Zheyan Luo · 2024
Closest in time.