ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2501.05309
  4. Cited By
Private Selection with Heterogeneous Sensitivities

Private Selection with Heterogeneous Sensitivities

10 January 2025
Daniela Antonova
Allegra Laro
Audra McMillan
Lorenz Wolf
ArXivPDFHTML

Papers citing "Private Selection with Heterogeneous Sensitivities"

15 / 15 papers shown
Title
Randomized algorithms for precise measurement of differentially-private,
  personalized recommendations
Randomized algorithms for precise measurement of differentially-private, personalized recommendations
Allegra Laro
Yanqing Chen
Hao He
Babak Aghazadeh
46
1
0
07 Aug 2023
Popularity Bias in Collaborative Filtering-Based Multimedia Recommender
  Systems
Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems
Dominik Kowald
Emanuel Lacić
23
21
0
01 Mar 2022
Generalized Linear Bandits with Local Differential Privacy
Generalized Linear Bandits with Local Differential Privacy
Yuxuan Han
Zhipeng Liang
Yang Wang
Jiheng Zhang
30
32
0
07 Jun 2021
The Permute-and-Flip Mechanism is Identical to Report-Noisy-Max with
  Exponential Noise
The Permute-and-Flip Mechanism is Identical to Report-Noisy-Max with Exponential Noise
Zeyu Ding
Daniel Kifer
S. Saghaian
Thomas Steinke
Yuxin Wang
Yingtai Xiao
Qiang Yan
28
26
0
15 May 2021
Permute-and-Flip: A new mechanism for differentially private selection
Permute-and-Flip: A new mechanism for differentially private selection
Ryan McKenna
Daniel Sheldon
130
49
0
23 Oct 2020
Local Differential Privacy for Regret Minimization in Reinforcement
  Learning
Local Differential Privacy for Regret Minimization in Reinforcement Learning
Evrard Garcelon
Vianney Perchet
Ciara Pike-Burke
Matteo Pirotta
55
35
0
15 Oct 2020
Locally Differentially Private (Contextual) Bandits Learning
Locally Differentially Private (Contextual) Bandits Learning
Kai Zheng
Tianle Cai
Weiran Huang
Zhenguo Li
Liwei Wang
41
65
0
01 Jun 2020
Embarrassingly Shallow Autoencoders for Sparse Data
Embarrassingly Shallow Autoencoders for Sparse Data
Harald Steck
116
249
0
08 May 2019
Private Selection from Private Candidates
Private Selection from Private Candidates
Jingcheng Liu
Kunal Talwar
36
130
0
19 Nov 2018
Differentially Private Contextual Linear Bandits
Differentially Private Contextual Linear Bandits
R. Shariff
Or Sheffet
41
119
0
28 Sep 2018
A Contextual Bandit Bake-off
A Contextual Bandit Bake-off
A. Bietti
Alekh Agarwal
John Langford
203
104
0
12 Feb 2018
Deep Learning with Differential Privacy
Deep Learning with Differential Privacy
Martín Abadi
Andy Chu
Ian Goodfellow
H. B. McMahan
Ilya Mironov
Kunal Talwar
Li Zhang
FedML
SyDa
175
6,069
0
01 Jul 2016
Communication-Efficient Learning of Deep Networks from Decentralized
  Data
Communication-Efficient Learning of Deep Networks from Decentralized Data
H. B. McMahan
Eider Moore
Daniel Ramage
S. Hampson
Blaise Agüera y Arcas
FedML
251
17,328
0
17 Feb 2016
Efficient Lipschitz Extensions for High-Dimensional Graph Statistics and
  Node Private Degree Distributions
Efficient Lipschitz Extensions for High-Dimensional Graph Statistics and Node Private Degree Distributions
Sofya Raskhodnikova
Adam D. Smith
34
41
0
29 Apr 2015
The Large Margin Mechanism for Differentially Private Maximization
The Large Margin Mechanism for Differentially Private Maximization
Kamalika Chaudhuri
Daniel J. Hsu
Shuang Song
64
38
0
07 Sep 2014
1