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Apprenticeship

This part of my life is called "The Pursuit of Doctorate". Here is my blog about learning process.

Diving In


Legend:

๐Ÿ“’ ๐Ÿ“ โŒจ๏ธ ๐Ÿ“ท ๐Ÿ’พ
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Supplementaries

Awesome Privacy Protection Research Materials

A curated list of awesome privacy protection research materials.

Courses:

Papers:

๐Ÿ”

Books

Title Authors Published in Year Files Notes Supplementaries
ๆ•ฐๆฎๅบ“็ณป็ปŸๆฆ‚่ฎบ ็Ž‹็Š, ่จๅธˆ็…Š ้ซ˜็ญ‰ๆ•™่‚ฒๅ‡บ็‰ˆ็คพ 2014 ๐Ÿ“
  • | The Algorithmic Foundations of Differential Privacy | Cynthia Dwork, Aaron Roth | TCS | 2014 | [:ledger:](https://www.nowpublishers.com/article/Details/TCS-042) | | [:floppy_disk:](http://www.cis.upenn.edu/~aaroth/courses/privacyF11.html) |

  • Jordi Soria-Comas, Josep Domingo-Ferrer: Optimal data-independent noise for differential privacy. Inf. Sci. 250: 200-214 (2013)

  • Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu: Differential Privacy and Applications. Advances in Information Security 69, Springer 2017, ISBN 978-3-319-62002-2, pp. 1-222

  • Ninghui Li, Min Lyu, Dong Su, Weining Yang: Differential Privacy: From Theory to Practice. Synthesis Lectures on Information Security, Privacy, & Trust, Morgan & Claypool Publishers 2016, pp. 1-138

๐Ÿ”

Tutorial

Instructors Institution Year Files Notes Supplementaries
The U.S. Census Bureau Adopts Differential Privacy John M. Abowd U.S. Census Bureau KDD 2018 ๐Ÿ“’ ๐Ÿ“ท
  • | The Algorithmic Foundations of Data Privacy | Aaron Roth | Penn | Fall 2011 | [:ledger:](http://www.cis.upenn.edu/~aaroth/courses/privacyF11.html) | | [:floppy_disk:](https://www.nowpublishers.com/article/Details/TCS-042) |

๐Ÿ”

Policy Brief

Title Authors Published in Year Files Notes Supplementaries
China's Social Credit System: A Mark of Progress or a Threat to Privacy? Martin Chorzempa, Paul Triolo, Samm Sacks 2018 ๐Ÿ“’ / ๐Ÿ’พ

๐Ÿ”

Survey

Title Authors Published in Year Files Notes Supplementaries
Differentially Private Data Publishing and Analysis: A Survey Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu TKDE 2017 ๐Ÿ“’
Privacy for Recommender Systems: Tutorial Abstract Bart P. Knijnenburg, Shlomo Berkovsky RecSys 2017 ๐Ÿ“’ ๐Ÿ“ท
Privacy in Location-Based Services: State-of-the-Art and Research Directions Mohamed F. Mokbel MDM 2007 ๐Ÿ“’ ๐Ÿ“ท

๐Ÿ”

Basic Techniques

Title Authors Published in Year Files Notes Supplementaries
Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms Fengxiang He, Bohan Wang, Dacheng Tao UAI 2021 ๐Ÿ“’ ๐Ÿ“
OSDP One-sided Differential Privacy Ios Kotsogiannis, Stelios Doudalis, Samuel Haney, Ashwin Machanavajjhala, Sharad Mehrotra ICDE 2020 ๐Ÿ“’ ๐Ÿ“ท๐Ÿ“ท
SVT-S Understanding the Sparse Vector Technique for Differential Privacy Min Lyu, Dong Su, Ninghui Li VLDB 2017 ๐Ÿ“’
Nearly-Optimal Private LASSO Kunal Talwar, Abhradeep Thakurta, Li Zhang NIPS 2015 ๐Ÿ“’ ๐Ÿ“ ๐Ÿ“’
Privacy-preserving statistical estimation with optimal convergence rates Adam D. Smith STOC 2011 ๐Ÿ“’
Smooth sensitivity and sampling in private data analysis Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith STOC 2007 ๐Ÿ“’

๐Ÿ”

Private Framework

Title Authors Published in Year Files Notes Supplementaries
KTELO KTELO: A Framework for Defining Differentially-Private Computations Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau SIGMOD 2018 ๐Ÿ“’ โŒจ๏ธ
  • PINQ: Frank McSherry: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. SIGMOD Conference 2009: 19-30; Davide Proserpio, Sharon Goldberg, Frank McSherry: Calibrating Data to Sensitivity in Private Data Analysis. PVLDB 7(8): 637-648 (2014)
  • Fuzz: Marco Gaboardi, Andreas Haeberlen, Justin Hsu, Arjun Narayan, Benjamin C. Pierce: Linear dependent types for differential privacy. POPL 2013: 357-370
  • PrivInfer: Gilles Barthe, Gian Pietro Farina, Marco Gaboardi, Emilio Jesรบs Gallego Arias, Andy Gordon, Justin Hsu, Pierre-Yves Strub: Differentially Private Bayesian Programming. ACM Conference on Computer and Communications Security 2016: 68-79
  • LightDP: Danfeng Zhang, Daniel Kifer: LightDP: towards automating differential privacy proofs. POPL 2017: 888-901

๐Ÿ”

Private Benchmark

  • DPBench: Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, Dan Zhang: Principled Evaluation of Differentially Private Algorithms using DPBench. SIGMOD Conference 2016: 139-154

๐Ÿ”

Private Data Publishing

  • | | Differentially private data publishing for data analysis | Dong Su | | 2016 | [:ledger:](https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=2220&context=open_access_dissertations) | | |
  • | JTree | Differentially Private High-Dimensional Data Publication via Sampling-Based Inference | Rui Chen, Qian Xiao, Yu Zhang, Jianliang Xu | KDD | 2015 | [:ledger:](https://www.comp.hkbu.edu.hk/~xujl/Papers/kdd15.pdf) | | |
  • | NoisyCut | Top-k frequent itemsets via differentially private FP-trees | Jaewoo Lee, Christopher W. Clifton | KDD | 2014 | [:ledger:](https://cybersecurity.uga.edu/publications/VI_KDD2014.pdf) | | |
  • | PTT<br>k-RecursiveMedians | Differentially Private Algorithms for Empirical Machine Learning | Ben Stoddard, Yan Chen, Ashwin Machanavajjhala | CoRR | 2014 | [:ledger:](https://arxiv.org/pdf/1411.5428.pdf) | | |
Transaction Data Publishing
Title Authors Published in Year Files Notes Supplementaries
PATE-GAN PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees James Jordon, Jinsung Yoon, Mihaela van der Schaar ICLR 2019 ๐Ÿ“’
Liancheng Privacy as a Service: Publishing Data and Models Ashish Dandekar, Debabrota Basu, Thomas Kister, Geong Sen Poh, Jia Xu, Stรฉphane Bressan DASFAA 2019 ๐Ÿ“’
A Data Publishing System Based on Privacy Preservation Zhihui Wang, Yun Zhu, Xuchen Zhou DASFAA 2019 ๐Ÿ“’
Approximate Query Processing using Deep Generative Models Saravanan Thirumuruganathan, Shohedul Hasan, Nick Koudas, Gautam Das CoRR 2019 ๐Ÿ“’
G-PATE Scalable Differentially Private Generative Student Model via PATE Yunhui Long, Suxin Lin, Zhuolin Yang, Carl A. Gunter, Bo Li CoRR 2019 ๐Ÿ“’
DP-GAN-DNN POSTER: A Unified Framework of Differentially Private Synthetic Data Release with Generative Adversarial Network Pei-Hsuan Lu, Chia-Mu Yu CCS 2017 ๐Ÿ“’ โŒจ๏ธ
PrivBayes PrivBayes: Private Data Release via Bayesian Networks Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, Xiaokui Xiao TODS
SIGMOD
2017
2014
๐Ÿ“’
๐Ÿ“’
โŒจ๏ธ
๐Ÿ“ท๐Ÿ“ท
Efficient privacy-preserving temporal and spacial data aggregation for smart grid communications Xiaolei Dong, Jun Zhou, Zhenfu Cao Concurrency 2016 ๐Ÿ“’ ๐Ÿ“

๐Ÿ”

Streaming Data Publishing
Title Authors Published in Year Files Notes Supplementaries
PeGaSus PeGaSus: Data-Adaptive Differentially Private Stream Processing Yan Chen, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau CCS 2017 ๐Ÿ“’ ๐Ÿ“
  • | CCDPSD | ๅผ‚ๆ–นๅทฎๅŠ ๅ™ชไธ‹ๅทฎๅˆ†้š็งๆตๆ•ฐๆฎๅ‘ๅธƒไธ€่‡ดๆ€งไผ˜ๅŒ–็ฎ—ๆณ• | ๅญ™ๅฒš, ๅบทๅฅ, ๅด่‹ฑๆฐ, ๅผ ็ซ‹็พค | ๆธ…ๅŽๅคงๅญฆๅญฆๆŠฅ | 2018 | [:ledger:](http://kns.cnki.net/KCMS/detail/11.2223.N.20180921.0900.001.html) | | |
  • | | ้ขๅ‘ๅฎžๆ—ถๆ•ฐๆฎๆต็š„ๅทฎๅˆ†้š็ง็›ดๆ–นๅ›พๅ‘ๅธƒๆŠ€ๆœฏ | ๆจๅบš, ๅคๆ˜ฅๅฉท, ็™ฝไบ‘็’ | ๅ—ไบฌ้‚ฎ็”ตๅคงๅญฆๅญฆๆŠฅ | 2018 | [:ledger:](http://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CJFDLAST2018&filename=NJYD201802014) | | |
  • | PrivTree | PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions | Jun Zhang, Xiaokui Xiao, Xing Xie | SIGMOD | 2016 | [:ledger:](http://delivery.acm.org/10.1145/2890000/2882928/p155-zhang.pdf) | | |

๐Ÿ”

Graph Data Publishing
Title Authors Published in Year Files Notes Supplementaries
GSN-DP Geo-social network publication based on differential privacy Xiaochun Wang, Yidong Li FCS 2018 ๐Ÿ“’ ๐Ÿ“ ๐Ÿ“ท

๐Ÿ”

Image Data Publishing
Title Authors Published in Year Files Notes Supplementaries
RPM Random permutation Maxout transform for cancellable facial template protection Andrew Beng Jin Teoh, Sejung Cho, Jihyeon Kim MTA 2018 ๐Ÿ“’ ๐Ÿ“
BEMK ้ขๅ‘ไบบ่„ธๅ›พๅƒๅ‘ๅธƒ็š„ๅทฎๅˆ†้š็งไฟๆŠค ๅผ ๅ•ธๅ‰‘, ไป˜่ช่ช, ๅญŸๅฐๅณฐ JIG 2018 ๐Ÿ“’ ๐Ÿ“
DPGAN Differentially Private Generative Adversarial Network Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, Jiayu Zhou CoRR 2018 ๐Ÿ“’ โŒจ๏ธ

๐Ÿ”

Private Data Analysis

Private Learning
Title Authors Published in Year Files Notes Supplementaries
Robust anomaly detection and backdoor attack detection via differential privacy Min Du, Ruoxi Jia, Dawn Song ICLR 2020 ๐Ÿ“’ ๐Ÿ“โŒจ๏ธ
Differentially Private Meta-Learning Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar ICLR 2020 ๐Ÿ“’ ๐Ÿ“
Privacy Enhanced Multimodal Neural Representations for Emotion Recognition Mimansa Jaiswal, Emily Mower Provost AAAI 2020 ๐Ÿ“’
Utility/Privacy Trade-off through the lens of Optimal Transport Etienne Boursier, Vianney Perchet AISTATS 2020 ๐Ÿ“’ โŒจ๏ธ
Understanding Gradient Clipping in Private SGD: A Geometric Perspective Xiangyi Chen, Zhiwei Steven Wu, Mingyi Hong NeurIPS 2020 ๐Ÿ“’ ๐Ÿ“๐Ÿ“๐Ÿ“ ๐Ÿ“’
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy Kareem Amin, Alex Kulesza, Andres Muรฑoz Medina, Sergei Vassilvitskii ICML 2019 ๐Ÿ“’ ๐Ÿ“๐Ÿ“ท๐Ÿ“ท
A General Approach to Adding Differential Privacy to Iterative Training Procedures H. Brendan McMahan, Galen Andrew CoRR 2018 ๐Ÿ“’ โŒจ๏ธ
AdaClip AdaCliP: Adaptive Clipping for Private SGD Venkatadheeraj Pichapati, Ananda Theertha Suresh, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar CoRR 2019 ๐Ÿ“’
Differentially Private Learning with Adaptive Clipping Om Thakkar, Galen Andrew, H. Brendan McMahan CoRR 2019 ๐Ÿ“’
DP-FedAvg Learning Differentially Private Recurrent Language Models H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang ICLR 2018 ๐Ÿ“’ ๐Ÿ“
Three Tools for Practical Differential Privacy Koen Lennart van der Veen, Ruben Seggers, Peter Bloem, Giorgio Patrini PPML 2018 ๐Ÿ“’
dp-GAN Differentially Private Releasing via Deep Generative Model Xinyang Zhang, Shouling Ji, Ting Wang CoRR 2018 ๐Ÿ“’ โŒจ๏ธ
Differentially Private Federated Learning: A Client Level Perspective Robin C. Geyer, Tassilo Klein, Moin Nabi CoRR 2017 ๐Ÿ“’ โŒจ๏ธ
๐Ÿ’พ
DPSGD Deep Learning with Differential Privacy Martรญn Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang CCS 2016 ๐Ÿ“’ ๐Ÿ’พ
โŒจ๏ธ

๐Ÿ”

Frequent Itemset Mining
Title Authors Published in Year Files Notes Supplementaries
Diff-FPM Mining frequent graph patterns with differential privacy Entong Shen, Ting Yu KDD 2013 ๐Ÿ“’ ๐Ÿ“ท
  • | PrivBasis | PrivBasis: Frequent Itemset Mining with Differential Privacy | Ninghui Li, Wahbeh H. Qardaji, Dong Su, Jianneng Cao | PVLDB | 2012 | [:ledger:](https://dl.acm.org/citation.cfm?id=2350251) | | [:keyboard:](https://github.com/DongSuIBM/PrivBasis) |

๐Ÿ”

Graph Data Analysis
Title Authors Published in Year Files Notes Supplementaries
FedGNN FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation Chuhan Wu, Fangzhao Wu, Yang Cao, Yongfeng Huang, Xing Xie CoRR 2021 ๐Ÿ“’
Adam-DP Privacy-Preserving Graph Convolutional Networks for Text Classification Timour Igamberdiev, Ivan Habernal CoRR 2021 ๐Ÿ“’
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models Amrita Roy Chowdhury, Theodoros Rekatsinas, Somesh Jha ICML 2020 ๐Ÿ“’ ๐Ÿ“’
PPKG Survey and Open Problems in Privacy Preserving Knowledge Graph: Merging, Query, Representation, Completion and Applications Chaochao Chen, Jamie Cui, Guanfeng Liu, Jia Wu, Li Wang CoRR 2020 ๐Ÿ“’
APGE Adversarial Privacy Preserving Graph Embedding against Inference Attack Kaiyang Li, Guangchun Luo, Yang Ye, Wei Li, Shihao Ji, Zhipeng Cai CoRR 2020 ๐Ÿ“’ โŒจ๏ธ
LPGNN Locally Private Graph Neural Networks Sina Sajadmanesh, Daniel Gatica-Perez CoRR 2020 ๐Ÿ“’ โŒจ๏ธ

๐Ÿ”

Recommender Systems
Title Authors Published in Year Files Notes Supplementaries
Extended PrivSR Towards privacy preserving social recommendation under personalized privacy settings Xuying Meng, Suhang Wang, Kai Shu, Jundong Li, Bo Chen, Huan Liu, Yujun Zhang WWWJ 2019 ๐Ÿ“’
FedMF Secure Federated Matrix Factorization Di Chai, Leye Wang, Kai Chen, Qiang Yang FML 2019 ๐Ÿ“’ ๐Ÿ“ โŒจ๏ธ
GD-DR Privacy Enhanced Matrix Factorization for Recommendation with Local Differential Privacy Hyejin Shin, Sungwook Kim, Junbum Shin, Xiaokui Xiao TKDE 2018 ๐Ÿ“’ ๐Ÿ“
PrivSR Personalized Privacy-Preserving Social Recommendation Xuying Meng, Suhang Wang, Kai Shu, Jundong Li, Bo Chen, Huan Liu, Yujun Zhang AAAI 2018 ๐Ÿ“’ ๐Ÿ“ ๐Ÿ’พ
DMF Privacy Preserving Point-of-Interest Recommendation Using Decentralized Matrix Factorization Chaochao Chen, Ziqi Liu, Peilin Zhao, Jun Zhou, Xiaolong Li AAAI 2018 ๐Ÿ“’ ๐Ÿ“
EpicRec EpicRec: Towards Practical Differentially Private Framework for Personalized Recommendation Yilin Shen, Hongxia Jin CCS 2016 ๐Ÿ“’
DPMF Differentially Private Matrix Factorization Jingyu Hua, Chang Xia, Sheng Zhong IJCAI 2015 ๐Ÿ“’ ๐Ÿ“
DP-UnP3R Privacy-Preserving Personalized Recommendation: An Instance-Based Approach via Differential Privacy Yilin Shen, Hongxia Jin ICDM 2014 ๐Ÿ“’

๐Ÿ”

Survival Analysis
Title Authors Published in Year Files Notes Supplementaries
Differentially Private Survival Function Estimation Lovedeep Gondara, Ke Wang MLHC 2020 ๐Ÿ“’ โŒจ๏ธ

๐Ÿ”

Natural Language Processing
Title Authors Published in Year Files Notes Supplementaries
Information Leakage in Embedding Models Congzheng Song, Ananth Raghunathan CCS 2020 ๐Ÿ“’ ๐Ÿ“ทโŒจ๏ธ
Privacy Risks of General-Purpose Language Models Xudong Pan, Mi Zhang, Shouling Ji, Min Yang IEEE Symposium on Security and Privacy ๐Ÿ“’
DPNR Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness Lingjuan Lyu, Xuanli He, Yitong Li EMNLP 2020 ๐Ÿ“’ โŒจ๏ธ
TextHide TextHide: Tackling Data Privacy for Language Understanding Tasks Yangsibo Huang, Zhao Song, Danqi Chen, Kai Li, Sanjeev Arora EMNLP 2020 ๐Ÿ“’ โŒจ๏ธ
PolicyQA PolicyQA: A Reading Comprehension Dataset for Privacy Policies Wasi Uddin Ahmad, Jianfeng Chi, Yuan Tian, Kai-Wei Chang EMNLP 2020 ๐Ÿ“’ โŒจ๏ธ
FedNewsRec Privacy-Preserving News Recommendation Model Learning Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, Xing Xie EMNLP 2020 ๐Ÿ“’ โŒจ๏ธ
MG-PriFair Multimodal Review Generation with Privacy and Fairness Awareness Xuan-Son Vu, Thanh-Son Nguyen, Duc-Trong Le, Lili Jiang COLING 2020 ๐Ÿ“’ ๐Ÿ“ท
Towards Privacy by Design in Learner Corpora Research: A Case of On-the-fly Pseudonymization of Swedish Learner Essays Elena Volodina, Yousuf Ali Mohammed, Sandra Derbring, Arild Matsson, Beรกta Megyesi COLING 2020 ๐Ÿ“’
OME Towards Differentially Private Text Representations Lingjuan Lyu, Yitong Li, Xuanli He, Tong Xiao SIGIR 2020 ๐Ÿ“’ ๐Ÿ“ โŒจ๏ธ

๐Ÿ”

Machine Unlearning

Title Authors Published in Year Files Notes Supplementaries
When Machine Unlearning Jeopardizes Privacy Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, Yang Zhang CCS 2021 ๐Ÿ“’ โŒจ๏ธ๐Ÿ“ท
Mixed-Linear Forgetting Mixed-Privacy Forgetting in Deep Networks Aditya Golatkar, Alessandro Achille, Avinash Ravichandran, Marzia Polito, Stefano Soatto CVPR 2021 ๐Ÿ“’ ๐Ÿ“’
Remember What You Want to Forget: Algorithms for Machine Unlearning Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh CoRR 2021 ๐Ÿ“’
GraphEraser Graph Unlearning Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, Yang Zhang CoRR 2021 ๐Ÿ“’
DeltaGrad DeltaGrad: Rapid retraining of machine learning models Yinjun Wu, Edgar Dobriban, Susan B. Davidson ICML 2020 ๐Ÿ“’ ๐Ÿ“’๐Ÿ“ท๐Ÿ“ทโŒจ๏ธ
Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks Aditya Golatkar, Alessandro Achille, Stefano Soatto CVPR 2020 ๐Ÿ“’ ๐Ÿ“’๐Ÿ“ทโŒจ๏ธ
Towards Probabilistic Verification of Machine Unlearning David Marco Sommer, Liwei Song, Sameer Wagh, Prateek Mittal CoRR 2020 ๐Ÿ“’ โŒจ๏ธ
Verifying that the influence of a user data point has been removed from a machine learning classifier Saurabh Shintre, Jasjeet Dhaliwal Patent 2018 ๐Ÿ“’

๐Ÿ”

Cryptography

Oblivious Transfer
Title Authors Published in Year Files Notes Supplementaries
Improved Private Set Intersection Against Malicious Adversaries Peter Rindal, Mike Rosulek EUROCRYPT 2017 ๐Ÿ“’ โŒจ๏ธ

๐Ÿ”

Searchable Encryption
Title Authors Published in Year Files Notes Supplementaries
SPiRiT Secure Search on Encrypted Data via Multi-Ring Sketch Adi Akavia, Dan Feldman, Hayim Shaul CCS 2018 ๐Ÿ“’ ๐Ÿ“
CPABKS Secure and Efficient Attribute-Based Encryption with Keyword Search Haijiang Wang, Xiaolei Dong, Zhenfu Cao, Dongmei Li CJ 2018 ๐Ÿ“’ ๐Ÿ“

๐Ÿ”

Encrypted Database
Title Authors Published in Year Files Notes Supplementaries
CryptZip Sa Wang, Yiwen Shao, Yungang Bao Practices of backuping homomorphically encrypted databases FCS 2019 ๐Ÿ“’ ๐Ÿ“ท
ไธ€็งๅŸบไบŽไฟๅฝขๅŠ ๅฏ†็š„ๅคงๆ•ฐๆฎ่„ฑๆ•็ณป็ปŸๅฎž็ŽฐๅŠ่ฏ„ไผฐ ๅž่ถ…่ฝถ, ๆœฑๅฐ‘ๆ•, ๅ‘จๆถ› ็”ตไฟก็ง‘ๅญฆ 2017 ๐Ÿ“’

๐Ÿ”

Encrypted Retrieval
Title Authors Published in Year Files Notes Supplementaries
Practical Approximate k Nearest Neighbor Queries with Location and Query Privacy Xun Yi, Russell Paulet, Elisa Bertino, Vijay Varadharajan TKDE 2016 ๐Ÿ“’
A Privacy-Preserving Framework for Large-Scale Content-Based Information Retrieval Li Weng, Laurent Amsaleg, April Morton, Stรฉphane Marchand-Maillet TIFS 2015 ๐Ÿ“’
Privacy-Preserving and Content-Protecting Location Based Queries Russell Paulet, Md. Golam Kaosar, Xun Yi, Elisa Bertino TKDE 2014 ๐Ÿ“’

๐Ÿ”

Encrypted Inference
Title Authors Published in Year Files Notes Supplementaries
CryptoNets CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin E. Lauter, Michael Naehrig, John Wernsing ICML 2016 ๐Ÿ“’

๐Ÿ”

Awesome Federated Learning Research Materials

A curated list of awesome federated learning research materials.

Courses:

Papers:

๐Ÿ”

Tutorial

Instructors Institution Year Files Notes Supplementaries
GDPR, Data Shortage and AI Qiang Yang HKUST 2019 ๐Ÿ“ท

๐Ÿ”

Survey

Title Authors Published in Year Files Notes Supplementaries
Federated Machine Learning: Concept and Applications Qiang Yang, Yang Liu, Tianjian Chen, Yongxin Tong TIST 2019 ๐Ÿ“’ ๐Ÿ“ ๐Ÿ’พ
Federated Learning Florian Hartmann 2018 ๐Ÿ“’ โŒจ๏ธโŒจ๏ธโŒจ๏ธ

๐Ÿ”

Algorithm

Communication
Title Authors Published in Year Files Notes Supplementaries
Efficient and Robust Asynchronous Federated Learning with Stragglers Ming Chen, Bingcheng Mao, Tianyi Ma CoRR 2020 ๐Ÿ“’

๐Ÿ”

Aggregation
Title Authors Published in Year Files Notes Supplementaries
q-FedAvg Fair Resource Allocation in Federated Learning Tian Li, Maziar Sanjabi, Virginia Smith ICLR 2020 ๐Ÿ“’
AFL Agnostic Federated Learning Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh ICML 2019 ๐Ÿ“’ ๐Ÿ’พ
๐Ÿ“ท
FedAvg Communication-Efficient Learning of Deep Networks from Decentralized Data Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agรผera y Arcas AISTATS 2017 ๐Ÿ“’ ๐Ÿ’พ

๐Ÿ”

Application
Title Authors Published in Year Files Notes Supplementaries
Federated CIFG Federated Learning for Mobile Keyboard Prediction Andrew Hard, Kanishka Rao, Rajiv Mathews, Franรงoise Beaufays, Sean Augenstein, Hubert Eichner, Chloรฉ Kiddon, Daniel Ramage CoRR 2018 ๐Ÿ“’ ๐Ÿ’พ
Federated Meta-Learning for Recommendation Fei Chen, Zhenhua Dong, Zhenguo Li, Xiuqiang He CoRR 2018 ๐Ÿ“’

๐Ÿ”

System

Title Authors Published in Year Files Notes Supplementaries
Towards Federated Learning at Scale: System Design Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloรฉ Kiddon, Jakub Konecnรฝ, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, Jason Roselander SysML 2019 ๐Ÿ“’

๐Ÿ”

Awesome Fairness Research Materials

A curated list of awesome fairness research materials.

Courses:

Papers:

๐Ÿ”

Tutorial

Instructors Institution Year Files Notes Supplementaries
Fairness and Control of Exposure in Two-sided Markets Thorsten Joachims ICTIR 2021 ๐Ÿ“’
Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned Sarah Bird, Ben Hutchinson, Krishnaram Kenthapadi, Emre Kฤฑcฤฑman, Margaret Mitchell Microsoft
Google
LinkedIn
KDD 2019 ๐Ÿ“ ๐Ÿ“ท
Challenges of incorporating algorithmic fairness into industry practice Henriette Cramer, Ken Holstein, Jenn Wortman Vaughan, Hal Daumรฉ III, Miroslav Dudรญk, Hanna Wallach, Sravana Reddy, Jean Garcia-Gathright Microsoft Research ACM FAccT 2019 ๐Ÿ“ท

๐Ÿ”

Causal Fairness

Instructors Institution Year Files Notes Supplementaries
Counterfactual reasoning in algorithmic fairness Ricardo Silva UCL
Alan Turing Institute
FairWare @ ICSE 2018 ๐Ÿ“ท
Fairness in Machine Learning and Its Causal Aspects Ricardo Silva UCL
Alan Turing Institute
2017 ๐Ÿ“ท

๐Ÿ”

Survey

Title Authors Published in Year Files Notes Supplementaries
Fairness-Aware Recommendation in Multi-Sided Platforms Masoud Mansoury WSDM 2021 ๐Ÿ“’
Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness Jessie Finocchiaro, Roland Maio, Faidra Monachou, Gourab K. Patro, Manish Raghavan, Ana-Andreea Stoica, Stratis Tsirtsis CoRR 2020 ๐Ÿ“’
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, Aram Galstyan CoRR 2019 ๐Ÿ“’
The Frontiers of Fairness in Machine Learning Alexandra Chouldechova, Aaron Roth CoRR 2018 ๐Ÿ“’

๐Ÿ”

Statistical Fairness

Title Authors Published in Year Files Notes Supplementaries
EquiTensors EquiTensors: Learning Fair Integrations of Heterogeneous Urban Data An Yan, Bill Howe SIGMOD 2021 ๐Ÿ“’ ๐Ÿ“ทโŒจ๏ธ
Constructing a Fair Classifier with Generated Fair Data Taeuk Jang, Feng Zheng, Xiaoqian Wang AAAI 2021 ๐Ÿ“’
CFair Conditional Learning of Fair Representations Han Zhao, Amanda Coston, Tameem Adel, Geoffrey J. Gordon ICLR 2020 ๐Ÿ“’ ๐Ÿ“ ๐Ÿ“
Fairness warnings
fair-MAML
Fairness warnings and fair-MAML: learning fairly with minimal data Dylan Slack, Sorelle A. Friedler, Emile Givental FAT* 2020 ๐Ÿ“’ ๐Ÿ“๐Ÿ“ท๐Ÿ“ทโŒจ๏ธ
AdvDebias Inherent Tradeoffs in Learning Fair Representations Han Zhao, Geoffrey J. Gordon NeurIPS 2019 ๐Ÿ“’ ๐Ÿ“ ๐Ÿ“๐Ÿ“๐Ÿ“๐Ÿ“ท๐Ÿ“ทโŒจ๏ธ
Random Repair Obtaining Fairness using Optimal Transport Theory Paula Gordaliza, Eustasio del Barrio, Fabrice Gamboa, Jean-Michel Loubes ICML 2019 ๐Ÿ“’ ๐Ÿ“’โŒจ๏ธ๐Ÿ“ท
FFVAE Flexibly Fair Representation Learning by Disentanglement Elliot Creager, David Madras, Jรถrn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel ICML 2019 ๐Ÿ“’ ๐Ÿ“’๐Ÿ“ท
DP-postprocessing
DP-oracle-learner
Differentially Private Fair Learning Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan Ullman ICML 2019 ๐Ÿ“’ ๐Ÿ“’๐Ÿ“ท
Fair Regression Fair Regression: Quantitative Definitions and Reduction-Based Algorithms Alekh Agarwal, Miroslav Dudรญk, Zhiwei Steven Wu ICML 2019 ๐Ÿ“’ ๐Ÿ“’โŒจ๏ธโŒจ๏ธ๐Ÿ“ท
Pairwise Fairness Fairness in Recommendation Ranking through Pairwise Comparisons Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow KDD 2019 ๐Ÿ“’ ๐Ÿ“ ๐Ÿ“ท
Strong Demographic Parity Wasserstein Fair Classification Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa UAI 2019 ๐Ÿ“’ ๐Ÿ“’โŒจ๏ธ
Exploring Human Gender Stereotypes with Word Association Test Yupei Du, Yuanbin Wu, Man Lan EMNLP 2019 ๐Ÿ“’ โŒจ๏ธ
AVD Penalizers
SD Penalizers
Penalizing Unfairness in Binary Classification Yahav Bechavod, Katrina Ligett CoRR 2017 ๐Ÿ“’ โŒจ๏ธ
Equalized Odds Equality of Opportunity in Supervised Learning Moritz Hardt, Eric Price, Nati Srebro NIPS 2016 ๐Ÿ“’ ๐Ÿ“’๐Ÿ“

๐Ÿ”

Causal Fairness

Title Authors Published in Year Files Notes Supplementaries
Counterfactual Privilege Making Decisions that Reduce Discriminatory Impacts Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva ICML 2019 ๐Ÿ“’ ๐Ÿ“’โŒจ๏ธ
๐Ÿ“ท๐Ÿ“ท
Fair K
Fair Add
Counterfactual Fairness Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva NIPS 2017 ๐Ÿ“’ ๐Ÿ“ ๐Ÿ“’โŒจ๏ธ๐Ÿ“ท๐Ÿ“ท
Multi-World Fairness When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness Chris Russell, Matt J. Kusner, Joshua R. Loftus, Ricardo Silva NIPS 2017 ๐Ÿ“’ ๐Ÿ“ ๐Ÿ“ท

๐Ÿ”

Resource Allocation

Title Authors Published in Year Files Notes Supplementaries
Slice Tuner Slice Tuner: A Selective Data Acquisition Framework for Accurate and Fair Machine Learning Models SIGMOD 2021 ๐Ÿ“’ ๐Ÿ“ท๐Ÿ“ทโŒจ๏ธ
TFROM TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers Yao Wu, Jian Cao, Guandong Xu, Yudong Tan SIGIR 2021 ๐Ÿ“’ ๐Ÿ“ท
FairBatch FairBatch: Batch Selection for Model Fairness Yuji Roh, Kangwook Lee, Steven Euijong Whang, Changho Suh ICLR 2021 ๐Ÿ“’ ๐Ÿ“ ๐Ÿ“ท๐Ÿ“ทโŒจ๏ธ
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information Pranjal Awasthi, Alex Beutel, Matthรคus Kleindessner, Jamie Morgenstern, Xuezhi Wang FAccT 2021 ๐Ÿ“’
TSFD Rank User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets Lequn Wang, Thorsten Joachims ICTIR 2021 ๐Ÿ“’
EARS Top-K Contextual Bandits with Equity of Exposure Olivier Jeunen, Bart Goethals RecSys 2021 ๐Ÿ“’ โŒจ๏ธ๐Ÿ“ท
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective Flavien Prost, Pranjal Awasthi, Nick Blumm, Aditee Kumthekar, Trevor Potter, Li Wei, Xuezhi Wang, Ed H. Chi, Jilin Chen, Alex Beutel AIES 2021 ๐Ÿ“’
ARL Fairness without Demographics through Adversarially Reweighted Learning Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed Chi NeurIPS 2020 ๐Ÿ“’ ๐Ÿ“๐Ÿ“๐Ÿ“๐Ÿ“’โŒจ๏ธ
FairCo Controlling Fairness and Bias in Dynamic Learning-to-Rank Marco Morik, Ashudeep Singh, Jessica Hong, Thorsten Joachims SIGIR 2020 ๐Ÿ“’ ๐Ÿ“๐Ÿ“ทโŒจ๏ธ
FairRec FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms Gourab K. Patro, Arpita Biswas, Niloy Ganguly, Krishna P. Gummadi, Abhijnan Chakraborty WWW 2020 ๐Ÿ“’ โŒจ๏ธ๐Ÿ“ท๐Ÿ“ท
Fair Updates in Two-Sided Market Platforms: On Incrementally Updating Recommendations Gourab K. Patro, Abhijnan Chakraborty, Niloy Ganguly, Krishna P. Gummadi AAAI 2020 ๐Ÿ“’ ๐Ÿ“ท
Equalized odds postprocessing under imperfect group information Pranjal Awasthi, Matthรคus Kleindessner, Jamie Morgenstern AISTATS 2020 ๐Ÿ“’ ๐Ÿ“’โŒจ๏ธ
Fair decision making using privacy-protected data David Pujol, Ryan McKenna, Satya Kuppam, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau FAT* 2020 ๐Ÿ“’ ๐Ÿ“ท๐Ÿ“ท
DPSGD-F Removing Disparate Impact of Differentially Private Stochastic Gradient Descent on Model Accuracy Depeng Xu, Wei Du, Xintao Wu CoRR 2020 ๐Ÿ“’
FEN Learning Fairness in Multi-Agent Systems Jiechuan Jiang, Zongqing Lu NeurIPS 2019 ๐Ÿ“’ ๐Ÿ“’๐Ÿ“๐Ÿ“๐Ÿ“โŒจ๏ธ
Differential Privacy Has Disparate Impact on Model Accuracy Eugene Bagdasaryan, Omid Poursaeed, Vitaly Shmatikov NeurIPS 2019 ๐Ÿ“’ ๐Ÿ“๐Ÿ“๐Ÿ“๐Ÿ“ทโŒจ๏ธ
DC
Maximin
Group-Fairness in Influence Maximization Alan Tsang, Bryan Wilder, Eric Rice, Milind Tambe, Yair Zick IJCAI 2019 ๐Ÿ“’ ๐Ÿ“โŒจ๏ธ
TREE01 Fairness without Harm: Decoupled Classifiers with Preference Guarantees Berk Ustun, Yang Liu, David C. Parkes ICML 2019 ๐Ÿ“’ ๐Ÿ“’โŒจ๏ธ๐Ÿ“ท
Greedy Capture
Local Capture
Proportionally Fair Clustering Xingyu Chen, Brandon Fain, Liang Lyu, Kamesh Munagala ICML 2019 ๐Ÿ“’ ๐Ÿ“โŒจ๏ธ๐Ÿ“ท
GF1A/B Group Fairness for the Allocation of Indivisible Goods Vincent Conitzer, Rupert Freeman, Nisarg Shah, Jennifer Wortman Vaughan AAAI 2019 ๐Ÿ“’ ๐Ÿ“
PCCS
TFCS
Fair Transfer Learning with Missing Protected Attributes Amanda Coston, Karthikeyan Natesan Ramamurthy, Dennis Wei, Kush R. Varshney, Skyler Speakman, Zairah Mustahsan, Supriyo Chakraborty AIES 2019 ๐Ÿ“’
FULTR Fair Learning-to-Rank from Implicit Feedback Himank Yadav, Zhengxiao Du, Thorsten Joachims CoRR 2019 ๐Ÿ“’
Fairness Without Demographics in Repeated Loss Minimization Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang ICML 2018 ๐Ÿ“’ ๐Ÿ“’โŒจ๏ธ

๐Ÿ”

Others

Stability
Title Authors Published in Year Files Notes Supplementaries
Stable-Fair Stable and Fair Classification Lingxiao Huang, Nisheeth K. Vishnoi ICML 2019 ๐Ÿ“’ ๐Ÿ“

๐Ÿ”

Awesome Blockchain Research Materials

A curated list of awesome blockchain research materials.

๐Ÿ”

Survey

Title Authors Published in Year Files Notes Supplementaries
Yellow Paper Ethereum: A Secure Decentralised Generalised Transaction Ledger Dr. Gavin Wood ๐Ÿ“’
้ป„็šฎไนฆ ไปฅๅคชๅŠ๏ผšไธ€็งๅฎ‰ๅ…จๅŽปไธญๅฟƒๅŒ–็š„้€š็”จไบคๆ˜“่ดฆๆœฌ ๅด”ๅนฟๆ–Œ, ้ซ˜ๅคฉ้œฒ ๐Ÿ“’
Untangling Blockchain: A Data Processing View of Blockchain Systems Tien Tuan Anh Dinh, Rui Liu, Meihui Zhang, Gang Chen, Beng Chin Ooi, Ji Wang TKDE 2018 ๐Ÿ“’ ๐Ÿ“ ๐Ÿ’พ
Making Sense of Blockchain Applications: A Typology for HCI Chris Elsden, Arthi Manohar, Jo Briggs, Mike Harding, Chris Speed, John Vines CHI 2018 ๐Ÿ“’ ๐Ÿ“
BigchainDB BigchainDB 2.0: The Blockchain Database BigchainDB BigchainDB 2018 ๐Ÿ“’ โŒจ๏ธ
BLOCKBENCH BLOCKBENCH: A Framework for Analyzing Private Blockchains Tien Tuan Anh Dinh, Ji Wang, Gang Chen, Rui Liu, Beng Chin Ooi, Kian-Lee Tan SIGMOD 2017 ๐Ÿ“’ โŒจ๏ธ
ๅŒบๅ—้“พ้š็งไฟๆŠค็ ”็ฉถ็ปผ่ฟฐ ็ฅ็ƒˆ็…Œ, ้ซ˜ๅณฐ, ๆฒˆ่’™, ๆŽ่‰ณไธœ, ้ƒ‘ๅฎๆ˜†, ๆฏ›ๆดชไบฎ, ๅด้œ‡ ่ฎก็ฎ—ๆœบ็ ”็ฉถไธŽๅ‘ๅฑ• 2017 ๐Ÿ“’ ๐Ÿ“

๐Ÿ”

Network Layer

Title Authors Published in Year Files Notes Supplementaries
BlockFL On-Device Federated Learning via Blockchain and its Latency Analysis Hyesung Kim, Jihong Park, Mehdi Bennis, Seong-Lyun Kim CoRR 2018 ๐Ÿ“’

๐Ÿ”

Other Awesome Research Materials

A curated list of awesome research materials.

๐Ÿ”

Artificial Intelligence

Title Authors Published in Year Files Notes Supplementaries
WebFace260M WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, Junjie Huang, Xinze Chen, Jiagang Zhu, Tian Yang, Jiwen Lu, Dalong Du, Jie Zhou CVPR 2021 ๐Ÿ“’ ๐Ÿ’พ
Trustworthy AI Jeannette M. Wing Commun. ACM 2021 ๐Ÿ“’ ๐Ÿ“ท
็ป†่Š‚ๅ†ณๅฎšๆˆ่ดฅ๏ผšๆŽจ่็ณป็ปŸๅฎž้ชŒๅๆ€ไธŽ่ฎจ่ฎบ ๆ–ฝ้Ÿถ้Ÿต, ็Ž‹ๆ™จ้˜ณ, ้ฉฌไธบไน‹, ๅผ ๆ•, ๅˆ˜ๅฅ•็พค, ้ฉฌๅฐ‘ๅนณ ไฟกๆฏๅฎ‰ๅ…จๅญฆๆŠฅ 2021 ๐Ÿ“’
Meta-Learning in Neural Networks: A Survey Timothy M. Hospedales, Antreas Antoniou, Paul Micaelli, Amos J. Storkey CoRR 2020 ๐Ÿ“’ ๐Ÿ“
AI Governance in 2019 a Year in Review Qian Shi, Hui Li, Brian Tse, John Hopcroft, Stuart Russell, Caroline Jeanmaire, Qiang Yang, Pascale Fung, Roman Yampolskiy, Allan Dafoe, Markus Anderljung, Gillian K. Hadfield, Jun Su, Thilo Hagendorff, Petra Ahrweiler, Robin Williams, Colin Allen, Poon King Wang, Ferran Jarabo Carbonell, Xiaohong Wang, Qingfeng Yang, Qi Yin, Don Wright, Miles Brundage, Jack Clark, Irene Solaiman, Gretchen Krueger, Seรกn ร“ hร‰igeartaigh, Helen Toner, Millie Liu, Steve Hoffman, Irakli Beridze, Wendell Wallach, Cyrus Hodes, Nicolas Miailhe, Jessica Cussins Newman, Dingding Chen, Eva Kaili, Francesca Rossi, Charlotte Stix, Angela Daly, Danit Gal, Arisa Ema, Goh Yihan, Nydia Remolina, Urvashi Aneja, Ying Fu, Zhiyun Zhao, Xiuquan Li, Weiwen Duan, Qun Luan, Rui Guo, Yingchun Wang Shanghai Institute for Science of Science 2020 ๐Ÿ“’ ๐Ÿ“
Meta-Weight-Net Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, Deyu Meng NeurIPS 2019 ๐Ÿ“’ ๐Ÿ“๐Ÿ“๐Ÿ“ ๐Ÿ“’โŒจ๏ธ
Graph Neural Networks for Natural Language Processing Shikhar Vashishth, Naganand Yadati, Partha Talukdar EMNLP 2019 ๐Ÿ“ทโŒจ๏ธ๐Ÿ“ท๐Ÿ“ท
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta, Natalia Dรญaz Rodrรญguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcรญa, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila, Francisco Herrera CoRR 2019 ๐Ÿ“’ ๐Ÿ“
ไปฅๆœบๅ™จๅญฆไน ็š„่ง†่ง’ๆฅ็œ‹ๆ—ถๅบ็‚น่ฟ‡็จ‹็š„ๆœ€ๆ–ฐ่ฟ›ๅฑ• ไธฅ้ช้ฉฐ ไธญๅ›ฝ่‡ชๅŠจๅŒ–ๅญฆไผšๆจกๅผ่ฏ†ๅˆซไธŽๆœบๅ™จๆ™บ่ƒฝไธ“ไธšๅง”ๅ‘˜ไผš้€š่ฎฏ 2019 ๐Ÿ“’
Temporal Point Processes and the Conditional Intensity Function Jakob Gulddahl Rasmussen CoRR 2018 ๐Ÿ“’
softImpute-ALS Matrix completion and low-rank SVD via fast alternating least squares Trevor Hastie, Rahul Mazumder, Jason D. Lee, Reza Zadeh JMLR 2015 ๐Ÿ“’ โŒจ๏ธโŒจ๏ธโŒจ๏ธ
Efficient Per-Example Gradient Computations Ian J. Goodfellow CoRR 2015 ๐Ÿ“’
RBO A similarity measure for indefinite rankings William Webber, Alistair Moffat, Justin Zobel TOIS 2010 ๐Ÿ“’
BPR BPR: Bayesian Personalized Ranking from Implicit Feedback Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme UAI 2009 ๐Ÿ“’
Damped Newton Algorithms for Matrix Factorization with Missing Data A. M. Buchanan, Andrew W. Fitzgibbon CVPR 2005 ๐Ÿ“’

๐Ÿ”

Robust Statistics

Title Authors Published in Year Files Notes Supplementaries
Influence Functions in Deep Learning Are Fragile Samyadeep Basu, Phillip Pope, Soheil Feizi ICLR 2021 ๐Ÿ“’ ๐Ÿ“ ๐Ÿ“ท
Group Influence Functions On Second-Order Group Influence Functions for Black-Box Predictions Samyadeep Basu, Xuchen You, Soheil Feizi ICML 2020 ๐Ÿ“’ ๐Ÿ“’
TracIn Estimating Training Data Influence by Tracing Gradient Descent Garima Pruthi, Frederick Liu, Satyen Kale, Mukund Sundararajan NeurIPS 2020 ๐Ÿ“’ ๐Ÿ“๐Ÿ“๐Ÿ“๐Ÿ“๐Ÿ“ ๐Ÿ“’โŒจ๏ธ
On the Accuracy of Influence Functions for Measuring Group Effects Pang Wei Koh, Kai-Siang Ang, Hubert H. K. Teo, Percy Liang NeurIPS 2019 ๐Ÿ“’ ๐Ÿ“๐Ÿ“๐Ÿ“ ๐Ÿ“’โŒจ๏ธโŒจ๏ธ๐Ÿ“ท
Representer Values Representer Point Selection for Explaining Deep Neural Networks Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, Pradeep Ravikumar NeurIPS 2018 ๐Ÿ“’ ๐Ÿ“๐Ÿ“ ๐Ÿ“’๐Ÿ“ทโŒจ๏ธ
Understanding Black-box Predictions via Influence Functions Pang Wei Koh, Percy Liang ICML 2017 ๐Ÿ“’ ๐Ÿ“’โŒจ๏ธโŒจ๏ธ๐Ÿ“ท

๐Ÿ”

Generalization

Title Authors Published in Year Files Notes Supplementaries
When is memorization of irrelevant training data necessary for high-accuracy learning? Gavin Brown, Mark Bun, Vitaly Feldman, Adam Smith, Kunal Talwar STOC 2021 ๐Ÿ“’
Understanding deep learning (still) requires rethinking generalization Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals CACM 2021 ๐Ÿ“’ ๐Ÿ“ ๐Ÿ“ท
Does Learning Require Memorization? A Short Tale about a Long Tail Vitaly Feldman STOC 2020 ๐Ÿ“’ ๐Ÿ“’๐Ÿ“ท๐Ÿ“ท๐Ÿ“ท
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation Vitaly Feldman, Chiyuan Zhang NeurIPS 2020 ๐Ÿ“’ ๐Ÿ“’๐Ÿ“๐Ÿ“๐Ÿ“โŒจ๏ธ
Understanding deep learning requires rethinking generalization Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals ICLR 2017 ๐Ÿ“’ ๐Ÿ“โŒจ๏ธ๐Ÿ“ท๐Ÿ“ท

๐Ÿ”

Large-Scale Optimization

Title Authors Published in Year Files Notes Supplementaries
BDA A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton Risheng Liu, Pan Mu, Xiaoming Yuan, Shangzhi Zeng, Jin Zhang ICML 2020 ๐Ÿ“’
AGD+ On Acceleration with Noise-Corrupted Gradients Michael Cohen, Jelena Diakonikolas, Lorenzo Orecchia ICML 2018 ๐Ÿ“’ ๐Ÿ’พ
  • Olivier Devolder, Franรงois Glineur, Yurii Nesterov: First-order methods of smooth convex optimization with inexact oracle. Math. Program. 146(1-2): 37-75 (2014)
  • Alexandre d'Aspremont: Smooth Optimization with Approximate Gradient. SIAM Journal on Optimization 19(3): 1171-1183 (2008)

๐Ÿ”

Combinatorial Optimization

Title Authors Published in Year Files Notes Supplementaries
GSLS Optimizing top-k retrieval: submodularity analysis and search strategies Chaofeng Sha, Keqiang Wang, Dell Zhang, Xiaoling Wang, Aoying Zhou FCS
WAIM
2016
2014
๐Ÿ“’
๐Ÿ“’
SubmEP Ensemble Pruning: A Submodular Function Maximization Perspective Chaofeng Sha, Keqiang Wang, Xiaoling Wang, Aoying Zhou DASFAA 2014 ๐Ÿ“’

๐Ÿ”

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