What Would Gauss Say About Representations? Probing Pretrained Image Models using Synthetic Gaussian Benchmarks
- Irene Ko
- Pin-Yu Chen
- et al.
- 2024
- ICML 2024
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47 results for AI Privacy
SalUn: Empowering Machine Unlearning via Gradient-Based Weight Saliency in Both Image Classification and Generation
- Chongyu Fan
- Jiancheng Liu
- et al.
- 2024
- ICLR 2024
- Topic
Differentially Private Stochastic Coordinate Descent
- Georgios Damaskinos
- Celestine Mendler-Dünner
- et al.
- 2021
- AAAI 2021
The Discrete Gaussian for Differential Privacy
- Clément L. Canonne
- Gautam Kamath
- et al.
- 2020
- NeurIPS 2020
New oracle-efficient algorithms for private synthetic data release
- Giuseppe Vietri
- Grace Tian
- et al.
- 2020
- ICML 2020
Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training
- Kristjan Greenewald
- Yuancheng Yu
- et al.
- 2024
- NeurIPS 2024
Securing Floating-Point Arithmetic for Noise Addition
- 2024
- CCS 2024
Ultra-Compact Computing at the Edge Involving Unobtrusively Small Sub-millimeter Heterogeneous Integration Packaging
- Frank Libsch
- Steve Bedell
- et al.
- 2024
- ECTC 2024
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks
- Chulin Xie
- Yunhui Long
- et al.
- 2023
- CCS 2023
Post-processing Private Synthetic Data for Improving Utility on Selected Measures
- 2023
- NeurIPS 2023