Search Results for author:
Found 11 papers, 5 papers with code
The BrowserGym Ecosystem for Web Agent Research
The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging automation and Large Language Models (LLMs).
WebCanvas: Benchmarking Web Agents in Online Environments
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
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
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.
Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning
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).
Causal Inference on Distribution Functions
Understanding causal relationships is one of the most important goals of modern science.
Causal Inference
Methodology
The Promises of Parallel Outcomes
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
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.
Covariance Estimation for Matrix-valued Data
Covariance estimation for matrix-valued data has received an increasing interest in applications.
Multi-cause causal inference with unmeasured confounding and binary outcome
Unobserved confounding presents a major threat to causal inference in observational studies.
Methodology
Matrix Linear Discriminant Analysis
We propose a novel linear discriminant analysis approach for the classification of high-dimensional matrix-valued data that commonly arises from imaging studies.
