Jindong Wang

papers

1,685

total citations

papers (17)

CompeteAI: Understanding the Competition Dynamics of Large Language Model-based Agents

ICML 2024arXiv

citations

Dynamic Evaluation of Large Language Models by Meta Probing Agents

ICML 2024arXiv

citations

SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer

CVPR 2025arXiv

citations

Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning

ICCV 2023arXiv

citations

The Good, The Bad, and Why: Unveiling Emotions in Generative AI

ICML 2024arXiv

citations

Open-Vocabulary Calibration for Fine-tuned CLIP

ICML 2024arXiv

citations

Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models

NEURIPS 2023arXiv

citations

Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup

ICML 2024arXiv

citations

A General Framework for Learning from Weak Supervision

ICML 2024arXiv

citations

SpecFormer: Guarding Vision Transformer Robustness via Maximum Singular Value Penalization

ECCV 2024arXiv

citations

Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation

CVPR 2020

citations

Learning Causal Semantic Representation for Out-of-Distribution Prediction

NEURIPS 2021arXiv

citations

Position: TrustLLM: Trustworthiness in Large Language Models

ICML 2024

citations

Jindong Wang

papers (17)

FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling

USB: A Unified Semi-supervised Learning Benchmark for Classification

CycleResearcher: Improving Automated Research via Automated Review

Position: What Can Large Language Models Tell Us about Time Series Analysis

CompeteAI: Understanding the Competition Dynamics of Large Language Model-based Agents

Dynamic Evaluation of Large Language Models by Meta Probing Agents

SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer

Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning

The Good, The Bad, and Why: Unveiling Emotions in Generative AI

Open-Vocabulary Calibration for Fine-tuned CLIP

Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models

Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup

A General Framework for Learning from Weak Supervision

SpecFormer: Guarding Vision Transformer Robustness via Maximum Singular Value Penalization

Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation

Learning Causal Semantic Representation for Out-of-Distribution Prediction

Position: TrustLLM: Trustworthiness in Large Language Models

papers (17)

FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling

USB: A Unified Semi-supervised Learning Benchmark for Classification

CycleResearcher: Improving Automated Research via Automated Review

Position: What Can Large Language Models Tell Us about Time Series Analysis

CompeteAI: Understanding the Competition Dynamics of Large Language Model-based Agents

Dynamic Evaluation of Large Language Models by Meta Probing Agents

SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer

Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning

The Good, The Bad, and Why: Unveiling Emotions in Generative AI

Open-Vocabulary Calibration for Fine-tuned CLIP

Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models

Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup

A General Framework for Learning from Weak Supervision

SpecFormer: Guarding Vision Transformer Robustness via Maximum Singular Value Penalization

Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation

Learning Causal Semantic Representation for Out-of-Distribution Prediction

Position: TrustLLM: Trustworthiness in Large Language Models