DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators.
Trans. Mach. Learn. Res., 2025
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations.
Proc. Priv. Enhancing Technol., 2024
Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
Fed-GLOSS-DP: Federated, Global Learning using Synthetic Sets with Record Level Differential Privacy.
CoRR, 2023
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training.
Proceedings of the International Conference on Machine Learning, 2022
Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021
InfoScrub: Towards Attribute Privacy by Targeted Obfuscation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021
Video Rescaling Networks With Joint Optimization Strategies for Downscaling and Upscaling.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021
All About Structure: Adapting Structural Information Across Domains for Boosting Semantic Segmentation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019
Learning Priors for Adversarial Autoencoders.
Proceedings of the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2018