Yonggang Zhang

papers

926

total citations

papers (18)

Prompt Distribution Learning

CVPR 2022arXiv

312

citations

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

NEURIPS 2022arXiv

200

citations

FedFed: Feature Distillation against Data Heterogeneity in Federated Learning

NEURIPS 2023arXiv

122

citations

Learning to Augment Distributions for Out-of-distribution Detection

NEURIPS 2023arXiv

citations

From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning

ICML 2024arXiv

citations

Yonggang Zhang

papers (18)

Prompt Distribution Learning

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

FedFed: Feature Distillation against Data Heterogeneity in Federated Learning

Learning to Augment Distributions for Out-of-distribution Detection

From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning

Interpreting and Improving Large Language Models in Arithmetic Calculation

Watermarking for Out-of-distribution Detection

Hard Sample Matters a Lot in Zero-Shot Quantization

Invariant Learning via Probability of Sufficient and Necessary Causes

FedImpro: Measuring and Improving Client Update in Federated Learning

Federated Learning with Extremely Noisy Clients via Negative Distillation

Robust Training of Federated Models with Extremely Label Deficiency

Towards Lightweight Black-Box Attack Against Deep Neural Networks

Detecting Generated Images by Fitting Natural Image Distributions

SODA: Robust Training of Test-Time Data Adaptors

Epistemic Uncertainty for Generated Image Detection

Class-Disentanglement and Applications in Adversarial Detection and Defense

Meta Convolutional Neural Networks for Single Domain Generalization

papers (18)

Prompt Distribution Learning

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

FedFed: Feature Distillation against Data Heterogeneity in Federated Learning

Learning to Augment Distributions for Out-of-distribution Detection

From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning

Interpreting and Improving Large Language Models in Arithmetic Calculation

Watermarking for Out-of-distribution Detection

Hard Sample Matters a Lot in Zero-Shot Quantization

Invariant Learning via Probability of Sufficient and Necessary Causes

FedImpro: Measuring and Improving Client Update in Federated Learning

Federated Learning with Extremely Noisy Clients via Negative Distillation

Robust Training of Federated Models with Extremely Label Deficiency

Towards Lightweight Black-Box Attack Against Deep Neural Networks

Detecting Generated Images by Fitting Natural Image Distributions

SODA: Robust Training of Test-Time Data Adaptors

Epistemic Uncertainty for Generated Image Detection

Class-Disentanglement and Applications in Adversarial Detection and Defense

Meta Convolutional Neural Networks for Single Domain Generalization