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Congzheng Song
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2020 – today
- 2024
- [c13]Kunal Talwar, Shan Wang, Audra McMillan, Vitaly Feldman, Pansy Bansal, Bailey Basile, Áine Cahill, Yi Sheng Chan, Mike Chatzidakis, Junye Chen, Oliver R. A. Chick, Mona Chitnis, Suman Ganta, Yusuf Goren, Filip Granqvist, Kristine Guo, Frederic Jacobs, Omid Javidbakht, Albert Liu, Richard Low, Dan Mascenik, Steve Myers, David Park, Wonhee Park, Gianni Parsa, Tommy Pauly, Christian Priebe, Rehan Rishi, Guy N. Rothblum, Congzheng Song, Linmao Song, Karl Tarbe, Sebastian Vogt, Shundong Zhou, Vojta Jina, Michael Scaria, Luke Winstrom:
Samplable Anonymous Aggregation for Private Federated Data Analysis. CCS 2024: 2859-2873 - [i21]Tao Yu, Congzheng Song, Jianyu Wang, Mona Chitnis:
Momentum Approximation in Asynchronous Private Federated Learning. CoRR abs/2402.09247 (2024) - [i20]Filip Granqvist, Congzheng Song, Áine Cahill, Rogier C. van Dalen, Martin Pelikan, Yi Sheng Chan, Xiaojun Feng, Natarajan Krishnaswami, Vojta Jina, Mona Chitnis:
pfl-research: simulation framework for accelerating research in Private Federated Learning. CoRR abs/2404.06430 (2024) - 2023
- [c12]Mingbin Xu, Congzheng Song, Ye Tian, Neha Agrawal, Filip Granqvist, Rogier C. van Dalen, Xiao Zhang, Arturo Argueta, Shiyi Han, Yaqiao Deng, Leo Liu, Anmol Walia, Alex Jin:
Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices. ICASSP 2023: 1-5 - [i19]Tatsuki Koga, Congzheng Song, Martin Pelikan, Mona Chitnis:
Population Expansion for Training Language Models with Private Federated Learning. CoRR abs/2307.07477 (2023) - [i18]Kunal Talwar, Shan Wang, Audra McMillan, Vojta Jina, Vitaly Feldman, Bailey Basile, Áine Cahill, Yi Sheng Chan, Mike Chatzidakis, Junye Chen, Oliver R. A. Chick, Mona Chitnis, Suman Ganta, Yusuf Goren, Filip Granqvist, Kristine Guo, Frederic Jacobs, Omid Javidbakht, Albert Liu, Richard Low, Dan Mascenik, Steve Myers, David Park, Wonhee Park, Gianni Parsa, Tommy Pauly, Christian Priebe, Rehan Rishi, Guy N. Rothblum, Michael Scaria, Linmao Song, Congzheng Song, Karl Tarbe, Sebastian Vogt, Luke Winstrom, Shundong Zhou:
Samplable Anonymous Aggregation for Private Federated Data Analysis. CoRR abs/2307.15017 (2023) - 2022
- [c11]Congzheng Song, Filip Granqvist, Kunal Talwar:
FLAIR: Federated Learning Annotated Image Repository. NeurIPS 2022 - [i17]Eugene Bagdasaryan, Congzheng Song, Rogier C. van Dalen, Matt Seigel, Áine Cahill:
Training a Tokenizer for Free with Private Federated Learning. CoRR abs/2203.09943 (2022) - [i16]Congzheng Song, Filip Granqvist, Kunal Talwar:
FLAIR: Federated Learning Annotated Image Repository. CoRR abs/2207.08869 (2022) - [i15]Mingbin Xu, Congzheng Song, Ye Tian, Neha Agrawal, Filip Granqvist, Rogier C. van Dalen, Xiao Zhang, Arturo Argueta, Shiyi Han, Yaqiao Deng, Leo Liu, Anmol Walia, Alex Jin:
Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices. CoRR abs/2207.08988 (2022) - [i14]Audra McMillan, Omid Javidbakht, Kunal Talwar, Elliot Briggs, Mike Chatzidakis, Junye Chen, John C. Duchi, Vitaly Feldman, Yusuf Goren, Michael Hesse, Vojta Jina, Anil Katti, Albert Liu, Cheney Lyford, Joey Meyer, Alex Palmer, David Park, Wonhee Park, Gianni Parsa, Paul Pelzl, Rehan Rishi, Congzheng Song, Shan Wang, Shundong Zhou:
Private Federated Statistics in an Interactive Setting. CoRR abs/2211.10082 (2022) - 2021
- [c10]Roei Schuster, Congzheng Song, Eran Tromer, Vitaly Shmatikov:
You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion. USENIX Security Symposium 2021: 1559-1575 - 2020
- [c9]Congzheng Song, Reza Shokri
:
Membership Encoding for Deep Learning. AsiaCCS 2020: 344-356 - [c8]Congzheng Song, Ananth Raghunathan:
Information Leakage in Embedding Models. CCS 2020: 377-390 - [c7]Congzheng Song, Alexander M. Rush
Adversarial Semantic Collisions. EMNLP (1) 2020: 4198-4210 - [c6]Congzheng Song, Vitaly Shmatikov:
Overlearning Reveals Sensitive Attributes. ICLR 2020 - [c5]Congzheng Song, Shanghang Zhang, Najmeh Sadoughi, Pengtao Xie, Eric P. Xing:
Generalized Zero-Shot Text Classification for ICD Coding. IJCAI 2020: 4018-4024 - [i13]Congzheng Song, Ananth Raghunathan:
Information Leakage in Embedding Models. CoRR abs/2004.00053 (2020) - [i12]Roei Schuster, Congzheng Song, Eran Tromer, Vitaly Shmatikov:
You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion. CoRR abs/2007.02220 (2020) - [i11]Congzheng Song, Alexander M. Rush, Vitaly Shmatikov:
Adversarial Semantic Collisions. CoRR abs/2011.04743 (2020)
2010 – 2019
- 2019
- [c4]Congzheng Song, Vitaly Shmatikov:
Auditing Data Provenance in Text-Generation Models. KDD 2019: 196-206 - [c3]Luca Melis, Congzheng Song, Emiliano De Cristofaro, Vitaly Shmatikov:
Exploiting Unintended Feature Leakage in Collaborative Learning. IEEE Symposium on Security and Privacy 2019: 691-706 - [i10]Congzheng Song, Vitaly Shmatikov:
Overlearning Reveals Sensitive Attributes. CoRR abs/1905.11742 (2019) - [i9]Congzheng Song, Reza Shokri:
Membership Encoding for Deep Learning. CoRR abs/1909.12982 (2019) - [i8]Congzheng Song, Shanghang Zhang, Najmeh Sadoughi, Pengtao Xie, Eric P. Xing:
Generalized Zero-shot ICD Coding. CoRR abs/1909.13154 (2019) - 2018
- [i7]Congzheng Song, Yiming Sun:
Kernel Distillation for Gaussian Processes. CoRR abs/1801.10273 (2018) - [i6]Congzheng Song, Vitaly Shmatikov:
Fooling OCR Systems with Adversarial Text Images. CoRR abs/1802.05385 (2018) - [i5]Tyler Hunt, Congzheng Song, Reza Shokri, Vitaly Shmatikov, Emmett Witchel:
Chiron: Privacy-preserving Machine Learning as a Service. CoRR abs/1803.05961 (2018) - [i4]Luca Melis, Congzheng Song, Emiliano De Cristofaro, Vitaly Shmatikov:
Inference Attacks Against Collaborative Learning. CoRR abs/1805.04049 (2018) - [i3]Congzheng Song, Vitaly Shmatikov:
The Natural Auditor: How To Tell If Someone Used Your Words To Train Their Model. CoRR abs/1811.00513 (2018) - 2017
- [c2]Congzheng Song, Thomas Ristenpart, Vitaly Shmatikov:
Machine Learning Models that Remember Too Much. CCS 2017: 587-601 - [c1]Reza Shokri
Membership Inference Attacks Against Machine Learning Models. IEEE Symposium on Security and Privacy 2017: 3-18 - [i2]Congzheng Song, Thomas Ristenpart, Vitaly Shmatikov:
Machine Learning Models that Remember Too Much. CoRR abs/1709.07886 (2017) - 2016
- [i1]Safoora Yousefi, Congzheng Song, Nelson Nauata, Lee Cooper:
Learning Genomic Representations to Predict Clinical Outcomes in Cancer. CoRR abs/1609.08663 (2016)
Coauthor Index
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last updated on 2024-12-11 20:46 CET by the dblp team
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