Zinan Lin | DeepAI

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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...

0 Xinyu Tang, et al. ∙

∙ 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 ...

0 Xuefei Ning, et al. ∙

∙ 06/20/2023

DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Generative Pre-trained Transformer (GPT) models have exhibited exciting ...

0 Boxin Wang, et al. ∙

∙ 06/15/2023

OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models

Diffusion probabilistic models (DPMs) are a new class of generative mode...

0 Enshu Liu, et al. ∙

∙ 05/24/2023

Differentially Private Synthetic Data via Foundation Model APIs 1: Images

Generating differentially private (DP) synthetic data that closely resem...

0 Zinan Lin, et al. ∙

∙ 05/23/2023

Selective Pre-training for Private Fine-tuning

Suppose we want to train text prediction models in email clients or word...

0 Da Yu, et al. ∙

∙ 03/03/2023

Summary Statistic Privacy in Data Sharing

Data sharing between different parties has become increasingly common ac...

0 Zinan Lin, et al. ∙

∙ 06/03/2022

On the Privacy Properties of GAN-generated Samples

The privacy implications of generative adversarial networks (GANs) are a...

0 Zinan Lin, et al. ∙

∙ 03/20/2022

RareGAN: Generating Samples for Rare Classes

We study the problem of learning generative adversarial networks (GANs) ...

0 Zinan Lin, et al. ∙

∙ 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...

0 Todd Huster, et al. ∙

∙ 01/13/2021

MLGO: a Machine Learning Guided Compiler Optimizations Framework

Leveraging machine-learning (ML) techniques for compiler optimizations h...

27 Mircea Trofin, et al. ∙

∙ 09/06/2020

Why Spectral Normalization Stabilizes GANs: Analysis and Improvements

Spectral normalization (SN) is a widely-used technique for improving the...

22 Zinan Lin, et al. ∙

∙ 09/30/2019

Generating High-fidelity, Synthetic Time Series Datasets with DoppelGANger

Limited data access is a substantial barrier to data-driven networking r...

16 Zinan Lin, et al. ∙

∙ 06/14/2019

InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive Regularizers

Training disentangled representations with generative adversarial networ...

3 Zinan Lin, et al. ∙

∙ 11/08/2018

Robustness of Conditional GANs to Noisy Labels

We study the problem of learning conditional generators from noisy label...

18 Kiran Koshy Thekumparampil, et al. ∙

∙ 12/12/2017

PacGAN: The power of two samples in generative adversarial networks

Generative adversarial networks (GANs) are innovative techniques for lea...

0 Zinan Lin, et al. ∙

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