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2007.05089
Cited By
The Trade-Offs of Private Prediction
9 July 2020
Laurens van der Maaten
Awni Y. Hannun
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Papers citing
"The Trade-Offs of Private Prediction"
20 / 20 papers shown
Privacy Preserving In-Context-Learning Framework for Large Language Models
Bishnu Bhusal
Manoj Acharya
R. Kaur
Colin Samplawski
Anirban Roy
Adam D. Cobb
Rohit Chadha
Susmit Jha
SyDa
445
0
0
17 Sep 2025
Mitigating Disparate Impact of Differentially Private Learning through Bounded Adaptive Clipping
Linzh Zhao
Aki Rehn
Mikko A. Heikkilä
Razane Tajeddine
Antti Honkela
265
3
0
02 Jun 2025
Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications
Marziyeh Mohammadi
Mohsen Vejdanihemmat
Mahshad Lotfinia
M. Rusu
Daniel Truhn
Andreas K. Maier
Soroosh Tayebi Arasteh
374
1
0
31 May 2025
KL-regularization Itself is Differentially Private in Bandits and RLHF
Yizhou Zhang
Kishan Panaganti
Laixi Shi
Juba Ziani
Adam Wierman
306
1
0
23 May 2025
Adaptively Private Next-Token Prediction of Large Language Models
James Flemings
Meisam Razaviyayn
Murali Annavaram
470
3
0
02 Oct 2024
Differentially Private Next-Token Prediction of Large Language Models
North American Chapter of the Association for Computational Linguistics (NAACL), 2024
James Flemings
Meisam Razaviyayn
Murali Annavaram
560
25
0
22 Mar 2024
Private Truly-Everlasting Robust-Prediction
International Conference on Machine Learning (ICML), 2024
Uri Stemmer
194
1
0
09 Jan 2024
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
269
22
0
21 Aug 2023
"Private Prediction Strikes Back!'' Private Kernelized Nearest Neighbors with Individual Renyi Filter
Conference on Uncertainty in Artificial Intelligence (UAI), 2023
Yuqing Zhu
Xuandong Zhao
Chuan Guo
Yu-Xiang Wang
264
5
0
12 Jun 2023
Harnessing large-language models to generate private synthetic text
Alexey Kurakin
Natalia Ponomareva
Umar Syed
Liam MacDermed
Seth Neel
SILM
SyDa
375
64
0
02 Jun 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
Journal of Artificial Intelligence Research (JAIR), 2023
Natalia Ponomareva
Hussein Hazimeh
Alexey Kurakin
Zheng Xu
Carson E. Denison
H. B. McMahan
Sergei Vassilvitskii
Steve Chien
Abhradeep Thakurta
597
263
0
01 Mar 2023
On Provable Copyright Protection for Generative Models
International Conference on Machine Learning (ICML), 2023
Nikhil Vyas
Sham Kakade
Boaz Barak
407
118
0
21 Feb 2023
Unlocking High-Accuracy Differentially Private Image Classification through Scale
Soham De
Leonard Berrada
Jamie Hayes
Samuel L. Smith
Borja Balle
522
274
0
28 Apr 2022
Optimal Membership Inference Bounds for Adaptive Composition of Sampled Gaussian Mechanisms
Saeed Mahloujifar
Alexandre Sablayrolles
Graham Cormode
S. Jha
172
23
0
12 Apr 2022
Submix: Practical Private Prediction for Large-Scale Language Models
Antonio A. Ginart
Laurens van der Maaten
James Zou
Chuan Guo
221
30
0
04 Jan 2022
MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layers
International Middleware Conference (Middleware), 2021
A. Boutet
Thomas LeBrun
Jan Aalmoes
Adrien Baud
FedML
288
20
0
26 Sep 2021
Privacy Assessment of Federated Learning using Private Personalized Layers
T. Jourdan
A. Boutet
Carole Frindel
FedML
255
8
0
15 Jun 2021
Fairness in the Eyes of the Data: Certifying Machine-Learning Models
AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2020
Shahar Segal
Yossi Adi
Benny Pinkas
Carsten Baum
C. Ganesh
Joseph Keshet
FedML
256
38
0
03 Sep 2020
Cryptanalytic Extraction of Neural Network Models
Annual International Cryptology Conference (CRYPTO), 2020
Nicholas Carlini
Matthew Jagielski
Ilya Mironov
FedML
MLAU
MIACV
AAML
523
156
0
10 Mar 2020
Machine Unlearning: Linear Filtration for Logit-based Classifiers
Machine-mediated learning (ML), 2020
Thomas Baumhauer
Pascal Schöttle
Matthias Zeppelzauer
MU
443
162
0
07 Feb 2020
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