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09/21/2023
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
We study the problem of in-context learning (ICL) with large language mo...
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07/28/2023
Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding
This work aims at decreasing the end-to-end generation latency of large ...
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06/20/2023
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Generative Pre-trained Transformer (GPT) models have exhibited exciting ...
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06/15/2023
OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models
Diffusion probabilistic models (DPMs) are a new class of generative mode...
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05/24/2023
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Generating differentially private (DP) synthetic data that closely resem...
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05/23/2023
Selective Pre-training for Private Fine-tuning
Suppose we want to train text prediction models in email clients or word...
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03/03/2023
Summary Statistic Privacy in Data Sharing
Data sharing between different parties has become increasingly common ac...
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06/03/2022
On the Privacy Properties of GAN-generated Samples
The privacy implications of generative adversarial networks (GANs) are a...
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03/20/2022
RareGAN: Generating Samples for Rare Classes
We study the problem of learning generative adversarial networks (GANs) ...
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01/22/2021
Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions
Generative adversarial networks (GANs) are often billed as "universal di...
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01/13/2021
MLGO: a Machine Learning Guided Compiler Optimizations Framework
Leveraging machine-learning (ML) techniques for compiler optimizations h...
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09/06/2020
Why Spectral Normalization Stabilizes GANs: Analysis and Improvements
Spectral normalization (SN) is a widely-used technique for improving the...
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09/30/2019
Generating High-fidelity, Synthetic Time Series Datasets with DoppelGANger
Limited data access is a substantial barrier to data-driven networking r...
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06/14/2019
InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive Regularizers
Training disentangled representations with generative adversarial networ...
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11/08/2018
Robustness of Conditional GANs to Noisy Labels
We study the problem of learning conditional generators from noisy label...
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12/12/2017
