Shuang Song

Affiliations:
  • Google Research, Brain
  • University of California, San Diego, USA


According to our database1, Shuang Song authored at least 30 papers between 2013 and 2024.

Collaborative distances:

Timeline

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Bibliography

2024
Private Learning with Public Features.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Challenges towards the Next Frontier in Privacy.
CoRR, 2023

Multi-Task Differential Privacy Under Distribution Skew.
Proceedings of the International Conference on Machine Learning, 2023

Measuring Forgetting of Memorized Training Examples.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Debugging Differential Privacy: A Case Study for Privacy Auditing.
CoRR, 2022

Toward Training at ImageNet Scale with Differential Privacy.
CoRR, 2022

Membership Inference Attacks From First Principles.
Proceedings of the 43rd IEEE Symposium on Security and Privacy, 2022

EANA: Reducing Privacy Risk on Large-scale Recommendation Models.
Proceedings of the RecSys '22: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18, 2022

Public Data-Assisted Mirror Descent for Private Model Training.
Proceedings of the International Conference on Machine Learning, 2022

2021
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning.
Proceedings of the 42nd IEEE Symposium on Security and Privacy, 2021

Differentially Private Model Personalization.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Practical and Private (Deep) Learning Without Sampling or Shuffling.
Proceedings of the 38th International Conference on Machine Learning, 2021

Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates.
Proceedings of the 38th International Conference on Machine Learning, 2021

Evading the Curse of Dimensionality in Unconstrained Private GLMs.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Tempered Sigmoid Activations for Deep Learning with Differential Privacy.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
An Attack on InstaHide: Is Private Learning Possible with Instance Encoding?
CoRR, 2020

Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems.
CoRR, 2020

Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation.
CoRR, 2020

The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
That which we call private.
CoRR, 2019

2018
Privacy-Preserving Algorithms for Machine Learning.
PhD thesis, 2018

Differentially Private Continual Release of Graph Statistics.
CoRR, 2018

Scalable Private Learning with PATE.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Pufferfish Privacy Mechanisms for Correlated Data.
Proceedings of the 2017 ACM International Conference on Management of Data, 2017

Renyi Differential Privacy Mechanisms for Posterior Sampling.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Composition properties of inferential privacy for time-series data.
Proceedings of the 55th Annual Allerton Conference on Communication, 2017

2016
Privacy-preserving Analysis of Correlated Data.
CoRR, 2016

2015
Learning from Data with Heterogeneous Noise using SGD.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
The Large Margin Mechanism for Differentially Private Maximization.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013
Stochastic gradient descent with differentially private updates.
Proceedings of the IEEE Global Conference on Signal and Information Processing, 2013


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