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Poster
Mon 1:00 Predicting Infectiousness for Proactive Contact Tracing
Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif B Muller, Meng Qu, victor schmidt, Pierre-luc St-charles, hannah alsdurf, Olexa Bilaniuk, david buckeridge, Gaétan Marceau Caron, pierre carrier, Joumana Ghosn, satya gagne, Chris J Pal, Irina Rish, Bernhard Schoelkopf, abhinav sharma, Jian Tang, Andrew Williams
Spotlight
Mon 14:00 Predicting Infectiousness for Proactive Contact Tracing
Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif B Muller, Meng Qu, victor schmidt, Pierre-luc St-charles, hannah alsdurf, Olexa Bilaniuk, david buckeridge, Gaétan Marceau Caron, pierre carrier, Joumana Ghosn, satya gagne, Chris J Pal, Irina Rish, Bernhard Schoelkopf, abhinav sharma, Jian Tang, Andrew Williams
Poster
Mon 17:00 Unlearnable Examples: Making Personal Data Unexploitable
Hanxun Huang, Daniel Ma, Sarah Erfani, James Bailey, Yisen Wang
Poster
Mon 17:00 Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification
Yingxue Zhou, Steven Wu, Arindam Banerjee
Poster
Mon 17:00 SAFENet: A Secure, Accurate and Fast Neural Network Inference
Qian Lou, Yilin Shen, Hongxia Jin, Lei Jiang
Spotlight
Mon 20:38 Information Laundering for Model Privacy
Xinran Wang, Yu Xiang, Jun Gao, Jie Ding
Poster
Tue 1:00 FedMix: Approximation of Mixup under Mean Augmented Federated Learning
Tehrim Yoon, Sumin Shin, Sung Ju Hwang, Eunho Yang
Poster
Tue 17:00 Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning
Haibo Yang, Minghong Fang, Jia Liu
Poster
Tue 17:00 Information Laundering for Model Privacy
Xinran Wang, Yu Xiang, Jun Gao, Jie Ding
Poster
Wed 9:00 Generative Time-series Modeling with Fourier Flows
Ahmed Alaa, Alex Chan, Mihaela van der Schaar
Spotlight
Wed 13:58 Differentially Private Learning Needs Better Features (or Much More Data)
Florian Tramer, Dan Boneh
Poster
Thu 1:00 Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning
Namyeong Kwon, Hwidong Na, Gabriel Huang, Simon Lacoste-Julien
Poster
Thu 1:00 R-GAP: Recursive Gradient Attack on Privacy
Junyi Zhu, Matthew Blaschko
Poster
Thu 1:00 Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning
Da Yu, Huishuai Zhang, Wei Chen, Tie-Yan Liu
Poster
Thu 1:00 Private Image Reconstruction from System Side Channels Using Generative Models
Yuanyuan Yuan, Shuai Wang, Junping Zhang
Spotlight
Thu 4:35 Unlearnable Examples: Making Personal Data Unexploitable
Hanxun Huang, Daniel Ma, Sarah Erfani, James Bailey, Yisen Wang
Poster
Thu 9:00 CaPC Learning: Confidential and Private Collaborative Learning
Christopher Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, Xiao Wang
Poster
Thu 9:00 Differentially Private Learning Needs Better Features (or Much More Data)
Florian Tramer, Dan Boneh
Poster
Thu 9:00 Private Post-GAN Boosting
Marcel Neunhoeffer, Steven Wu, Cynthia Dwork
Poster
Thu 9:00 Dataset Meta-Learning from Kernel Ridge-Regression
Timothy Nguyen, Zhourong Chen, Jaehoon Lee
Poster
Thu 17:00 FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, Qi Dou
Workshop
Fri 4:45 Hardware-Aware Efficient Training of Deep Learning Models
Ghouthi BOUKLI HACENE, Vincent Gripon, François Leduc-Primeau, Vahid Partovi Nia, Fan Yang, Andreas Moshovos, Yoshua Bengio
Workshop
Fri 7:00 Synthetic Data Generation: Quality, Privacy, Bias
Sergul Aydore, Krishnaram Kenthapadi, Haipeng Chen, Edward Choi, Jamie Hayes, Mario Fritz, Rachel Cummings, Krishnaram Kenthapadi
Fri 7:00 Secular Trends in ML Research with Morgan Stanley and Friends
Workshop
Fri 8:30 Workshop on Distributed and Private Machine Learning
Fatemeh Mireshghallah, Praneeth Vepakomma, Ayush Chopra, Vivek Sharma, Abhishek Singh, Adam Smith, Ramesh Raskar, Gautam Kamath, Reza Shokri
Workshop
Fri 9:01 "Differentially Private Synthetic Data Generations Using Generative Adversarial Networks" by Jinsung Yoon, Google Cloud AI
Jinsung Yoon
Workshop
Fri 9:10 Frequency Estimation in Local and Multiparty Differential Privacy
Graham Cormode
Workshop
Fri 9:35 Q&A for Frequency Estimation in Local and Multiparty Differential Privacy
Workshop
Fri 9:40 Inference Risks for Machine Learning
David Evans
Workshop
Fri 10:20 Break + Posters
Workshop
Fri 10:42 Privacy Amplification via Iteration for Shuffled and Online PNSGD
Matteo Sordello, Zhiqi Bu, Jinshuo Dong, Weijie J Su
Workshop
Fri 10:54 TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption
Ayoub Benaissa
Workshop
Fri 12:13 Introduction for A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via f-Divergences
Workshop
Fri 12:15 A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via f-Divergences
Lalitha Sankar
Workshop
Fri 12:40 Q&A for A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via f-Divergences
Workshop
AsymmetricML: An Asymmetric Decomposition Framework for Privacy-Preserving DNN Training and Inference
Yue Niu, Salman Avestimehr
Workshop
Layer-wise Characterization of Latent Information Leakage in Federated Learning
Fan Mo, Anastasia Borovykh, Mohammad Malekzadeh, Hamed Haddadi, Soteris Demetriou
Workshop
CAUSALLY CONSTRAINED DATA SYNTHESIS FOR PRIVATE DATA RELEASE
Varun Chandrasekaran, Darren Edge, Somesh Jha, Amit Sharma, Cheng Zhang, Shruti Tople
Workshop
MPCLeague: Robust 4-party Computation for Privacy-Preserving Machine Learning
Nishat Koti, Arpita Patra, Ajith Suresh
Workshop
Privacy Amplification via Iteration for Shuffled and Online PNSGD
Matteo Sordello, Zhiqi Bu, Jinshuo Dong, Weijie J Su
Workshop
Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT
Antti Koskela, Antti Honkela
Workshop
Privacy and Integrity Preserving Training Using Trusted Hardware
Seyedeh Hanieh Hashemi, Yongqin Wang, Murali Annavaram
Workshop
TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption
Ayoub Benaissa
Workshop
UNDERSTANDING CLIPPED FEDAVG: CONVERGENCE AND CLIENT-LEVEL DIFFERENTIAL PRIVACY
Xinwei Zhang, Xiangyi Chen, Jinfeng Yi, Steven Wu, Mingyi Hong
Workshop
Meta Federated Learning
Omid Aramoon, Gang Qu, Pin-Yu Chen, Yuan Tian
Workshop
Does Differential Privacy Defeat Data Poisoning?
Matthew Jagielski, Alina Oprea
Workshop
Distributed Gaussian Differential Privacy Via Shuffling
Kan Chen, Qi Long
Workshop
Towards Prior-Free Approximately Truthful One-Shot Auction Learning via Differential Privacy
Daniel Reusche, Nicolás Della Penna
Workshop
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Yu Rong, Junzhou Huang, Murali Annavaram, Salman Avestimehr
Workshop
Differentially Private Multi-Task Learning
Shengyuan Hu, Steven Wu, Virginia Smith
Workshop
Syft: A Platform for Universally Deployable Structured Transparency
Adam Hall
Workshop
FedPandemic: A Cross-Device Federated Learning Approach Towards Elementary Prognosis of Diseases During a Pandemic
Aman Priyanshu, Rakshit Naidu
Workshop
Towards Causal Federated Learning - For enhanced robustness and privacy
Sreya Francis
Workshop
Heterogeneous Zero-Shot Federated Learning with New Classes for Audio Classification
Gautham Krishna Gudur, Satheesh Perepu
Workshop
SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning
Nishat Koti, Mahak Pancholi, Arpita Patra, Ajith Suresh
Workshop
DEEP GRADIENT ATTACK WITH STRONG DP-SGD LOWER BOUND FOR LABEL PRIVACY
Sen Yuan
Workshop
Byzantine-Robust and Privacy-Preserving Framework for FedML
Seyedeh Hanieh Hashemi
Workshop
On Privacy and Confidentiality of Communications in Organizational Graphs
Masoumeh Shafieinejad, Huseyin Inan, Marcello Hasegawa, Robert Sim