Search Results for author: Dehan Kong

Found 10 papers, 4 papers with code

WebCanvas: Benchmarking Web Agents in Online Environments

1 code implementation18 Jun 2024 Yichen Pan, Dehan Kong, Sida Zhou, Cheng Cui, Yifei Leng, Bing Jiang, Hangyu Liu, Yanyi Shang, Shuyan Zhou, Tongshuang Wu, Zhengyang Wu

To bridge this gap, we introduce WebCanvas, an innovative online evaluation framework for web agents that effectively addresses the dynamic nature of web interactions.


General Phrase Debiaser: Debiasing Masked Language Models at a Multi-Token Level

1 code implementation23 Nov 2023 Bingkang Shi, Xiaodan Zhang, Dehan Kong, Yulei Wu, Zongzhen Liu, Honglei Lyu, Longtao Huang

The social biases and unwelcome stereotypes revealed by pretrained language models are becoming obstacles to their application.

From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework

1 code implementation29 May 2023 Yangyi Chen, Hongcheng Gao, Ganqu Cui, Lifan Yuan, Dehan Kong, Hanlu Wu, Ning Shi, Bo Yuan, Longtao Huang, Hui Xue, Zhiyuan Liu, Maosong Sun, Heng Ji

In our experiments, we conduct a robustness evaluation of RoBERTa models to demonstrate the effectiveness of our evaluation framework, and further show the rationality of each component in the framework.

Adversarial Attack

Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning

no code implementations26 May 2023 Ruixiang Tang, Dehan Kong, Longtao Huang, Hui Xue

Large language models (LLMs) have recently shown great potential for in-context learning, where LLMs learn a new task simply by conditioning on a few input-label pairs (prompts).

In-Context Learning

Causal Inference on Distribution Functions

no code implementations5 Jan 2021 Zhenhua Lin, Dehan Kong, Linbo Wang

Understanding causal relationships is one of the most important goals of modern science.

Causal Inference Methodology

The Promises of Parallel Outcomes

no code implementations10 Dec 2020 Ying Zhou, Dehan Kong, Linbo Wang

In contrast to existing proposals in the literature, the roles of multiple outcomes in our key identification assumption are symmetric, hence the name parallel outcomes.

Causal Inference Methodology

Federated Learning for Computational Pathology on Gigapixel Whole Slide Images

1 code implementation21 Sep 2020 Ming Y. Lu, Dehan Kong, Jana Lipkova, Richard J. Chen, Rajendra Singh, Drew F. K. Williamson, Tiffany Y. Chen, Faisal Mahmood

In this paper, we introduce privacy-preserving federated learning for gigapixel whole slide images in computational pathology using weakly-supervised attention multiple instance learning and differential privacy.

Federated Learning Multiple Instance Learning +4

Covariance Estimation for Matrix-valued Data

no code implementations11 Apr 2020 Yichi Zhang, Weining Shen, Dehan Kong

Covariance estimation for matrix-valued data has received an increasing interest in applications.

Multi-cause causal inference with unmeasured confounding and binary outcome

no code implementations31 Jul 2019 Dehan Kong, Shu Yang, Linbo Wang

Unobserved confounding presents a major threat to causal inference in observational studies.


Matrix Linear Discriminant Analysis

no code implementations24 Sep 2018 Wei Hu, Weining Shen, Hua Zhou, Dehan Kong

We propose a novel linear discriminant analysis approach for the classification of high-dimensional matrix-valued data that commonly arises from imaging studies.

General Classification regression

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