Fetching the paper…
Reading the bibliography…
Contextual bandit is a general framework for online learning in sequential decision-making problems that has found application in a wide range of domains, including recommendation systems, online advertising, and clinical trials.
A method for obtaining digital signatures and public-key cryptosystems
Ronald L Rivest, Adi Shamir, and Leonard Adleman · 1978
Earlier work this paper cites.
Constructions in a polynomial ring over the ring of integers
Abraham Seidenberg · 1978
Earlier work this paper cites.
A public key cryptosystem and a signature scheme based on discrete logarithms
Taher ElGamal · 1985
Earlier work this paper cites.
Topics in Matrix Analysis
Roger A. Horn and Charles R. Johnson · 1991
Earlier work this paper cites.
Public-key cryptosystems based on composite degree residuosity classes
Pascal Paillier · 1999
Earlier work this paper cites.
On the complexity of computing determinants
Erich Kaltofen and Gilles Villard · 2005
Earlier work this paper cites.
A schur–newton method for the matrix \ \backslash boldmath p th root and its inverse
Chun-Hua Guo and Nicholas J Higham · 2006
Earlier work this paper cites.
A fully homomorphic encryption scheme , volume 20
Craig Gentry and Dan Boneh · 2009
Earlier work this paper cites.
On lattices, learning with errors, random linear codes, and cryptography
Oded Regev · 2009
Earlier work this paper cites.
Improved algorithms for linear stochastic bandits
Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári · 2011
Earlier work this paper cites.
Contextual bandits with linear payoff functions
Wei Chu, Lihong Li, Lev Reyzin, and Robert E. Schapire · 2011
Earlier work this paper cites.
Fully homomorphic encryption without modulus switching from classical gapsvp
Zvika Brakerski · 2012
Earlier work this paper cites.
Multiparty computation from somewhat homomorphic encryption
Ivan Damgård, Valerio Pastro, Nigel P. Smart, and Sarah Zakarias · 2012
Earlier work this paper cites.
(leveled) fully homomorphic encryption without bootstrapping
Zvika Brakerski, Craig Gentry, and Vinod Vaikuntanathan · 2014
Earlier work this paper cites.
Parallelizing exploration-exploitation tradeoffs in gaussian process bandit optimization
Thomas Desautels, Andreas Krause, and Joel W Burdick · 2014
Earlier work this paper cites.
On the concrete hardness of learning with errors
Martin R Albrecht, Rachel Player, and Sam Scott · 2015
Earlier work this paper cites.
Machine learning classification over encrypted data
Raphael Bost, Raluca Ada Popa, Stephen Tu, and Shafi Goldwasser · 2015
Earlier work this paper cites.
Fhew: bootstrapping homomorphic encryption in less than a second
Léo Ducas and Daniele Micciancio · 2015
Earlier work this paper cites.
Fractional max-pooling, 2015
Benjamin Graham · 2015
Cited alongside, same era.
Trusted execution environment: what it is, and what it is not
Mohamed Sabt, Mohammed Achemlal, and Abdelmadjid Bouabdallah · 2015
Cited alongside, same era.
Batched bandit problems
Vianney Perchet, Philippe Rigollet, Sylvain Chassang, Erik Snowberg, et al · 2016
Cited alongside, same era.
Algorithms for differentially private multi-armed bandits
Aristide C. Y. Tossou and Christos Dimitrakakis · 2016
Cited alongside, same era.
Homomorphic encryption for arithmetic of approximate numbers
Jung Hee Cheon, Andrey Kim, Miran Kim, and Yongsoo Song · 2017
Cited alongside, same era.
Encrypted accelerated least squares regression
Pedro M Esperança, Louis JM Aslett, and Chris C Holmes · 2017
https://palisade-crypto.org/ , September 2020
PALISADE Lattice Cryptography Library (release 1.10.4) · 2020
Later among the works it cites.
Towards the alexnet moment for homomorphic encryption: Hcnn, thefirst homomorphic cnn on encrypted data with gpus, 2020
Ahmad Al Badawi, Jin Chao, Jie Lin, Chan Fook Mun, Jun Jie Sim, Benjamin Hong Meng Tan, Xiao Nan, Khin Mi Mi Aung, and Vijay Ramaseshan Chandrasekhar · 2020
Later among the works it cites.
Online decision making with high-dimensional covariates
Hamsa Bastani and Mohsen Bayati · 2020
Later among the works it cites.
Secure large-scale genome-wide association studies using homomorphic encryption
Marcelo Blatt, Alexander Gusev, Yuriy Polyakov, and Shafi Goldwasser · 2020
Later among the works it cites.
Near-linear time gaussian process optimization with adaptive batching and resparsification
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, and Lorenzo Rosasco · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Homomorphic encryption
Shai Halevi · 2017
Cited alongside, same era.
A survey on homomorphic encryption schemes: Theory and implementation
Abbas Acar, Hidayet Aksu, A. Selcuk Uluagac, and Mauro Conti · 2018
Cited alongside, same era.
Homomorphic encryption security standard
Martin Albrecht, Melissa Chase, Hao Chen, Jintai Ding, Shafi Goldwasser, Sergey Gorbunov, Shai Halevi, Jeffrey Hoffstein, Kim Laine, Kristin Lauter, Satya Lokam, Daniele Micciancio, Dustin Moody, Travis Morrison, Amit Sahai, and Vinod Vaikuntanathan · 2018
Cited alongside, same era.
Secure outsourced matrix computation and application to neural networks
Xiaoqian Jiang, Miran Kim, Kristin Lauter, and Yongsoo Song · 2018
Cited alongside, same era.
Contextual multi-armed bandits for causal marketing
Neela Sawant, Chitti Babu Namballa, Narayanan Sadagopan, and Houssam Nassif · 2018
Cited alongside, same era.
Differentially private contextual linear bandits
Roshan Shariff and Or Sheffet · 2018
Cited alongside, same era.
Efficient homomorphic comparison methods with optimal complexity
Jung Hee Cheon, Dongwoo Kim, and Duhyeong Kim · 2020
Later among the works it cites.
Secure cumulative reward maximization in linear stochastic bandits
Radu Ciucanu, Anatole Delabrouille, Pascal Lafourcade, and Marta Soare · 2020
Later among the works it cites.
Multinomial logit bandit with low switching cost
Kefan Dong, Yingkai Li, Qin Zhang, and Yuan Zhou · 2020
Later among the works it cites.
Sequential batch learning in finite-action linear contextual bandits
Yanjun Han, Zhengqing Zhou, Zhengyuan Zhou, Jose Blanchet, Peter W Glynn, and Yinyu Ye · 2020
Later among the works it cites.
Bandit algorithms
Tor Lattimore and Csaba Szepesvári · 2020
Later among the works it cites.
Linear bandits with limited adaptivity and learning distributional optimal design
Yufei Ruan, Jiaqi Yang, and Yuan Zhou · 2020
Later among the works it cites.
Global and local differential privacy for collaborative bandits
Huazheng Wang, Qian Zhao, Qingyun Wu, Shubham Chopra, Abhinav Khaitan, and Hongning Wang · 2020
Later among the works it cites.
Locally differentially private (contextual) bandits learning
Kai Zheng, Tianle Cai, Weiran Huang, Zhenguo Li, and Liwei Wang · 2020
Later among the works it cites.
Federated bandit: A gossiping approach
Zhaowei Zhu, Jingxuan Zhu, Ji Liu, and Yang Liu · 2020
Later among the works it cites.
Manipulation attacks in local differential privacy
Albert Cheu, Adam D. Smith, and Jonathan R. Ullman · 2021
Closest in time.
Panel: Humans and technology for inclusive privacy and security, 2021
Sanchari Das, Robert S. Gutzwiller, Rod D. Roscoe, Prashanth Rajivan, Yang Wang, L. Jean Camp, and Roberto Hoyle · 2021
Closest in time.
360norvic: 360-degree video classification from mobile encrypted video traffic
Chamara Kattadige, Aravindh Raman, Kanchana Thilakarathna, Andra Lutu, and Diego Perino · 2021
Closest in time.