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Training data privacy is a fundamental problem in modern Artificial Intelligence (AI) applications, such as face recognition, recommendation systems, language generation, and many others, as it may contain sensitive user information related to legal issues.
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Neural tangent kernel: Convergence and generalization in neural networks
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Learning overparameterized neural networks via stochastic gradient descent on structured data
Yuanzhi Li and Yingyu Liang · 2018
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Gaussian process behaviour in wide deep neural networks
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Stochastic gradient descent optimizes over-parameterized deep relu networks
Difan Zou, Yuan Cao, Dongruo Zhou, and Quanquan Gu · 2018
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Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks
Sanjeev Arora, Simon Du, Wei Hu, Zhiyuan Li, and Ruosong Wang · 2019
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On exact computation with an infinitely wide neural net
Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, Russ R Salakhutdinov, and Ruosong Wang · 2019
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Learning and generalization in overparameterized neural networks, going beyond two layers
Zeyuan Allen-Zhu, Yuanzhi Li, and Yingyu Liang · 2019
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A convergence theory for deep learning via over-parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2019
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On the convergence rate of training recurrent neural networks
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2019
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How much over-parameterization is sufficient to learn deep relu networks?
Zixiang Chen, Yuan Cao, Difan Zou, and Quanquan Gu · 2019
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Generalization bounds of stochastic gradient descent for wide and deep neural networks
Yuan Cao and Quanquan Gu · 2019
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On lazy training in differentiable programming
Lenaic Chizat, Edouard Oyallon, and Francis Bach · 2019
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Gradient descent finds global minima of deep neural networks
Simon Du, Jason Lee, Haochuan Li, Liwei Wang, and Xiyu Zhai · 2019
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Gradient descent provably optimizes over-parameterized neural networks
Simon S Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh · 2019
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A new analysis of differential privacy’s generalization guarantees
Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Moshe Shenfeld · 2019
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Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks
Ziwei Ji and Matus Telgarsky · 2019
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Wide neural networks of any depth evolve as linear models under gradient descent
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Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit
Song Mei, Theodor Misiakiewicz, and Andrea Montanari · 2019
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Bayesian convolutional neural networks with many channels are gaussian processes
Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Daniel A Abolafia, Jeffrey Pennington, and Jascha Sohl-Dickstein · 2019
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Generalization guarantees for neural networks via harnessing the low-rank structure of the jacobian
Samet Oymak, Zalan Fabian, Mingchen Li, and Mahdi Soltanolkotabi · 2019
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Overparameterized nonlinear learning: Gradient descent takes the shortest path?
Samet Oymak and Mahdi Soltanolkotabi · 2019
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Quadratic suffices for over-parametrization via matrix chernoff bound
Zhao Song and Xin Yang · 2019
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Greg Yang · 2019
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An improved analysis of training over-parameterized deep neural networks
Difan Zou and Quanquan Gu · 2019
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Deep learning based recommender system: A survey and new perspectives
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay · 2019
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Differentially private sketches for jaccard similarity estimation
Martin Aumüller, Anders Bourgeat, and Jana Schmurr · 2020
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Implicit bias of gradient descent for wide two-layer neural networks trained with the logistic loss
Lenaic Chizat and Francis Bach · 2020
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Towards understanding the spectral bias of deep learning, 2020
Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, and Quanquan Gu · 2020
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Differentially private release of synthetic graphs
Marek Eliáš, Michael Kapralov, Janardhan Kulkarni, and Yin Tat Lee · 2020
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Tight analysis of privacy and utility tradeoff in approximate differential privacy
Quan Geng, Wei Ding, Ruiqi Guo, and Sanjiv Kumar · 2020
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Disentangling feature and lazy training in deep neural networks
Mario Geiger, Stefano Spigler, Arthur Jacot, and Matthieu Wyart · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
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Generalized leverage score sampling for neural networks
Jason D Lee, Ruoqi Shen, Zhao Song, Mengdi Wang, et al · 2020
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Toward moderate overparameterization: Global convergence guarantees for training shallow neural networks
Samet Oymak and Mahdi Soltanolkotabi · 2020
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Gradient descent optimizes over-parameterized deep relu networks
Difan Zou, Yuan Cao, Dongruo Zhou, and Quanquan Gu · 2020
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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
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Training (overparametrized) neural networks in near-linear time
Jan van den Brand, Binghui Peng, Zhao Song, and Omri Weinstein · 2021
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Numerical composition of differential privacy
Sivakanth Gopi, Yin Tat Lee, and Lukas Wutschitz · 2021
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Differentially private one permutation hashing and bin-wise consistent weighted sampling
Xiaoyun Li and Ping Li · 2023
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Federated adversarial learning: A framework with convergence analysis
Xiaoxiao Li, Zhao Song, and Jiaming Yang · 2023
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A kernel-based view of language model fine-tuning
Sadhika Malladi, Alexander Wettig, Dingli Yu, Danqi Chen, and Sanjeev Arora · 2023
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Better and simpler lower bounds for differentially private statistical estimation
Shyam Narayanan · 2023
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How to dp-fy ml: A practical guide to machine learning with differential privacy
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H Brendan McMahan, Sergei Vassilvitskii, Steve Chien, and Abhradeep Guha Thakurta · 2023
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Lora: Low-rank adaptation of large language models
Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al · 2021
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Approximate range counting under differential privacy
Ziyue Huang and Ke Yi · 2021
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When machine learning meets privacy: A survey and outlook
Bo Liu, Ming Ding, Sina Shaham, Wenny Rahayu, Farhad Farokhi, and Zihuai Lin · 2021
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Gradient descent on two-layer nets: Margin maximization and simplicity bias
Kaifeng Lyu, Zhiyuan Li, Runzhe Wang, and Sanjeev Arora · 2021
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Large language models can be strong differentially private learners
Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto · 2021
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A theoretical analysis on feature learning in neural networks: Emergence from inputs and advantage over fixed features
Zhenmei Shi, Junyi Wei, and Yingyu Liang · 2021
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Does preprocessing help training over-parameterized neural networks?
Zhao Song, Shuo Yang, and Ruizhe Zhang · 2021
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Is solving graph neural tangent kernel equivalent to training graph neural network?
Lianke Qin, Zhao Song, and Baocheng Sun · 2023
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The trade-off between universality and label efficiency of representations from contrastive learning
Zhenmei Shi, Jiefeng Chen, Kunyang Li, Jayaram Raghuram, Xi Wu, Yingyu Liang, and Somesh Jha · 2023
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Domain generalization via nuclear norm regularization
Zhenmei Shi, Yifei Ming, Ying Fan, Frederic Sala, and Yingyu Liang · 2023
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When and how does known class help discover unknown ones? provable understanding through spectral analysis
Yiyou Sun, Zhenmei Shi, Yingyu Liang, and Yixuan Li · 2023
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Sketching for first order method: efficient algorithm for low-bandwidth channel and vulnerability
Zhao Song, Yitan Wang, Zheng Yu, and Lichen Zhang · 2023
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Efficient asynchronize stochastic gradient algorithm with structured data
Zhao Song and Mingquan Ye · 2023
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Sketching meets differential privacy: fast algorithm for dynamic kronecker projection maintenance
Zhao Song, Xin Yang, Yuanyuan Yang, and Lichen Zhang · 2023
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Dolfin: Diffusion layout transformers without autoencoder
Yilin Wang, Zeyuan Chen, Liangjun Zhong, Zheng Ding, Zhizhou Sha, and Zhuowen Tu · 2023
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On the inherent privacy properties of discrete denoising diffusion models
Rongzhe Wei, Eleonora Kreačić, Haoyu Wang, Haoteng Yin, Eli Chien, Vamsi K Potluru, and Pan Li · 2023
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Tokencompose: Grounding diffusion with token-level supervision
Zirui Wang, Zhizhou Sha, Zheng Ding, Yilin Wang, and Zhuowen Tu · 2023
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On robust streaming for learning with experts: algorithms and lower bounds
David Woodruff, Fred Zhang, and Samson Zhou · 2023
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Local differential privacy and its applications: A comprehensive survey
Mengmeng Yang, Taolin Guo, Tianqing Zhu, Ivan Tjuawinata, Jun Zhao, and Kwok-Yan Lam · 2023
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Efficiently computing similarities to private datasets
Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, and Jakub Tarnawski · 2024
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Hsr-enhanced sparse attention acceleration, 2024
Bo Chen, Yingyu Liang, Zhizhou Sha, Zhenmei Shi, and Zhao Song · 2024
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Continual observation of joins under differential privacy
Wei Dong, Zijun Chen, Qiyao Luo, Elaine Shi, and Ke Yi · 2024
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k k -means clustering with distance-based privacy
Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, and Peilin Zhong · 2024
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Privacy preserving prompt engineering: A survey
Kennedy Edemacu and Xintao Wu · 2024
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k-median clustering via metric embedding: towards better initialization with differential privacy
Chenglin Fan, Ping Li, and Xiaoyun Li · 2024
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Jiuxiang Gu, Chenyang Li, Yingyu Liang, Zhenmei Shi, and Zhao Song · 2024
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Toward infinite-long prefix in transformer
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Unraveling the smoothness properties of diffusion models: A gaussian mixture perspective
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A sublinear adversarial training algorithm
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Differentially private attention computation
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Neural network-based score estimation in diffusion models: Optimization and generalization
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Haibo Jin, Leyang Hu, Xinuo Li, Peiyan Zhang, Chonghan Chen, Jun Zhuang, and Haohan Wang · 2024
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Differentially private kernel density estimation
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Smooth flipping probability for differential private sign random projection methods
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Differentially private zeroth-order methods for scalable large language model finetuning
Zhihao Liu, Jian Lou, Wenjie Bao, Zhan Qin, and Kui Ren · 2024
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Fast john ellipsoid computation with differential privacy optimization
Xiaoyu Li, Yingyu Liang, Zhenmei Shi, Zhao Song, and Junwei Yu · 2024
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Looped relu mlps may be all you need as practical programmable computers, 2024
Yingyu Liang, Zhizhou Sha, Zhenmei Shi, Zhao Song, and Yufa Zhou · 2024
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Multi-layer transformers gradient can be approximated in almost linear time
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Differential privacy of cross-attention with provable guarantee
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Whispered tuning: Data privacy preservation in fine-tuning llms through differential privacy
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A graph-theoretic framework for understanding open-world semi-supervised learning
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Provable guarantees for neural networks via gradient feature learning
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Training multi-layer over-parametrized neural network in subquadratic time
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Evaluating the design space of diffusion-based generative models
Yuqing Wang, Ye He, and Molei Tao · 2024
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Enhancing jailbreak attack against large language models through silent tokens
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