Fine-Tuning with Differential Privacy Necessitates an Additional Hyperparameter Search

5 October 2022

Yannis Cattan

Christopher A. Choquette-Choo

Nicolas Papernot

Abhradeep Thakurta

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Papers citing "Fine-Tuning with Differential Privacy Necessitates an Additional Hyperparameter Search"

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NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA Marlon Tobaben Mohamed Ali Souibgui Rubèn Pérez Tito Khanh Nguyen Raouf Kerkouche ... Josep Lladós Ernest Valveny Antti Honkela Mario Fritz Dimosthenis Karatzas FedML 39 0 0 06 Nov 2024
DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning Jonathan Lebensold Maziar Sanjabi Pietro Astolfi Adriana Romero Soriano Kamalika Chaudhuri Mike Rabbat Chuan Guo DiffM 21 4 0 21 Mar 2024
Privacy-Aware Document Visual Question Answering Rubèn Pérez Tito Khanh Nguyen Marlon Tobaben Raouf Kerkouche Mohamed Ali Souibgui ... Lei Kang Ernest Valveny Antti Honkela Mario Fritz Dimosthenis Karatzas 35 13 0 15 Dec 2023
Unlocking Accuracy and Fairness in Differentially Private Image Classification Leonard Berrada Soham De J. Shen Jamie Hayes Robert Stanforth David Stutz Pushmeet Kohli Samuel L. Smith Borja Balle 21 13 0 21 Aug 2023
Guiding The Last Layer in Federated Learning with Pre-Trained Models G. Legate Nicolas Bernier Lucas Page-Caccia Edouard Oyallon Eugene Belilovsky FedML 11 8 0 06 Jun 2023
On the Fairness Impacts of Private Ensembles Models Cuong Tran Ferdinando Fioretto 39 4 0 19 May 2023
Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance Xin Gu Gautam Kamath Zhiwei Steven Wu 17 12 0 02 Mar 2023
Differentially Private Diffusion Models Generate Useful Synthetic Images Sahra Ghalebikesabi Leonard Berrada Sven Gowal Ira Ktena Robert Stanforth Jamie Hayes Soham De Samuel L. Smith Olivia Wiles Borja Balle DiffM 25 69 0 27 Feb 2023
Bounding Training Data Reconstruction in DP-SGD Jamie Hayes Saeed Mahloujifar Borja Balle AAML FedML 33 39 0 14 Feb 2023
On the Efficacy of Differentially Private Few-shot Image Classification Marlon Tobaben Aliaksandra Shysheya J. Bronskill Andrew J. Paverd Shruti Tople Santiago Zanella Béguelin Richard E. Turner Antti Honkela 33 11 0 02 Feb 2023
A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization Ashwinee Panda Xinyu Tang Saeed Mahloujifar Vikash Sehwag Prateek Mittal 36 11 0 08 Dec 2022
Differentially Private Image Classification from Features Harsh Mehta Walid Krichene Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky 46 7 0 24 Nov 2022
Learning to Generate Image Embeddings with User-level Differential Privacy Zheng Xu Maxwell D. Collins Yuxiao Wang Liviu Panait Sewoong Oh S. Augenstein Ting Liu Florian Schroff H. B. McMahan FedML 30 29 0 20 Nov 2022
Differentially Private Fine-tuning of Language Models Da Yu Saurabh Naik A. Backurs Sivakanth Gopi Huseyin A. Inan ... Y. Lee Andre Manoel Lukas Wutschitz Sergey Yekhanin Huishuai Zhang 134 346 0 13 Oct 2021
Hyperparameter Tuning with Renyi Differential Privacy Nicolas Papernot Thomas Steinke 132 119 0 07 Oct 2021
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning Da Yu Huishuai Zhang Wei Chen Tie-Yan Liu FedML SILM 94 110 0 25 Feb 2021
High-Performance Large-Scale Image Recognition Without Normalization Andrew Brock Soham De Samuel L. Smith Karen Simonyan VLM 223 512 0 11 Feb 2021
Tempered Sigmoid Activations for Deep Learning with Differential Privacy Nicolas Papernot Abhradeep Thakurta Shuang Song Steve Chien Ulfar Erlingsson AAML 139 178 0 28 Jul 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 231 4,460 0 23 Jan 2020
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 141 420 0 29 Nov 2018