Chen Zhao

The prestack seismic stochastic inversion based on quantum Metropolis–Hastings method

2016

Improving the resolution of seismic inversion is vital for the seismic inversion problems. Seismic stochastic inversion can make use of the high frequency information of logging data and improve the vertical resolution. The Markov chain …

Research on Authorship Attribution of Article Fragments via RNNs

2018

Most approaches for authorship attribution are based on traditional machine learning methods. Although these methods are effective, it is difficult for human to choose the features. we present a new model for authorship attribution by …

A Nested Bi-level Optimization Framework for Robust Few Shot Learning

2022 · Proceedings of the AAAI Conference on Artificial Intelligence

Model-Agnostic Meta-Learning (MAML), a popular gradient-based meta-learning framework, assumes that the contribution of each task or instance to the meta-learner is equal.Hence, it fails to address the domain shift between base and novel classes in …

Re-Examining Calibration: The Case of Question Answering

2022 · arXiv (Cornell University)

For users to trust model predictions, they need to understand model outputs, particularly their confidence - calibration aims to adjust (calibrate) models' confidence to match expected accuracy. We argue that the traditional calibration evaluation does …

An Automated Vulnerability Detection Framework for Smart Contracts

2023 · arXiv (Cornell University)

With the increase of the adoption of blockchain technology in providing decentralized solutions to various problems, smart contracts have become more popular to the point that billions of US Dollars are currently exchanged every day …

CRIMINAL ACTION DATA INFORMATION SYSTEM AT THE POLICE OFFICE

2022 · Journal of Technology and Data Science

The Indonesian National Police (POLRI) is a law enforcement security apparatus always ready to serve all people who need help and protection from all crimes. The Abung Semuli sector, located on Jalan Semuli Raya, Abung …

Retrieval-Augmented Chain-of-Thought in Semi-structured Domains

2023

Applying existing question answering (QA) systems to specialized domains like law and finance presents challenges that necessitate domain expertise. Although large language models (LLMs) have shown impressive language comprehension and in-context learning capabilities, their inability …

SHIFT3D: Synthesizing Hard Inputs For Tricking 3D Detectors

2023

We present SHIFT3D, a differentiable pipeline for generating 3D shapes that are structurally plausible yet challenging to 3D object detectors. In safety-critical applications like autonomous driving, discovering such novel challenging objects can offer insight into …

FADE: Towards Fairness-aware Data Generation for Domain Generalization via Classifier-Guided Score-based Diffusion Models

2024

Fairness-aware domain generalization (FairDG) has emerged as a critical challenge for deploying trustworthy AI systems, particularly in scenarios involving distribution shifts. Traditional methods for addressing fairness have failed in domain generalization due to their lack …

GDDA: Semantic OOD Detection on Graphs under Covariate Shift via Score-Based Diffusion Models

2024 · arXiv (Cornell University)

Out-of-distribution (OOD) detection poses a significant challenge for Graph Neural Networks (GNNs), particularly in open-world scenarios with varying distribution shifts. Most existing OOD detection methods on graphs primarily focus on identifying instances in test data …

Evidence-Based Out-of-Distribution Detection on Multi-Label Graphs

2025 · Society for Industrial and Applied Mathematics eBooks

The Out-of-Distribution (OOD) problem in graph-structured data is becoming increasingly important in various areas of research and applications, including social network recommendation [36], protein function detection[9, 21], etc. Furthermore, owing to the inherent multi-label properties …

Fairness across domains: a unified fairness-aware framework for domain generalization and unsupervised adaptation

2026 · Frontiers in Big Data

Fairness in machine learning remains a critical challenge, particularly in the presence of domain shift. We propose a unified fairness-aware framework for both domain generalization (DG) and unsupervised domain adaptation (UDA), which jointly addresses domain …