Yi Chang

A Novel Cascade Binary Tagging Framework for Relational Triple Extraction

2020

Extracting relational triples from unstructured text is crucial for large-scale knowledge graph construction. However, few existing works excel in solving the overlapping triple problem where multiple relational triples in the same sentence share the same …

A Unified Collaborative Representation Learning for Neural-Network Based Recommender Systems

2021 · IEEE Transactions on Knowledge and Data Engineering

With the boosting of neural networks, recommendation methods become significantly improved by their powerful ability of prediction and inference. Existing neural-network based recommender systems (NN-RSs) usually first employ matrix embedding (ME) as a pre-process to …

Handling Inter-class and Intra-class Imbalance in Class-imbalanced Learning

2021 · arXiv (Cornell University)

Class-imbalance is a common problem in machine learning practice. Typical Imbalanced Learning (IL) methods balance the data via intuitive class-wise resampling or reweighting. However, previous studies suggest that beyond class-imbalance, intrinsic data difficulty factors like …

Instructed Diffuser with Temporal Condition Guidance for Offline Reinforcement Learning

2023 · arXiv (Cornell University)

Recent works have shown the potential of diffusion models in computer vision and natural language processing. Apart from the classical supervised learning fields, diffusion models have also shown strong competitiveness in reinforcement learning (RL) by …

Large Language Model Evaluation via Matrix Nuclear-Norm

2024 · arXiv (Cornell University)

As large language models (LLMs) continue to evolve, efficient evaluation metrics are vital for assessing their ability to compress information and reduce redundancy. While traditional metrics like Matrix Entropy offer valuable insights, they are computationally …

XTRUST: On the Multilingual Trustworthiness of Large Language Models

2024 · arXiv (Cornell University)

Large language models (LLMs) have demonstrated remarkable capabilities across a range of natural language processing (NLP) tasks, capturing the attention of both practitioners and the broader public. A key question that now preoccupies the AI …

Streaming Recommender Systems

2017

The increasing popularity of real-world recommender systems produces data continuously and rapidly, and it becomes more realistic to study recommender systems under streaming scenarios. Data streams present distinct properties such as temporally ordered, continuous and …

Ranking Relevance in Yahoo Search

2016

Search engines play a crucial role in our daily lives. Relevance is the core problem of a commercial search engine. It has attracted thousands of researchers from both academia and industry and has been studied …

Recommendation with Social Dimensions

2016 · Proceedings of the AAAI Conference on Artificial Intelligence

The pervasive presence of social media greatly enriches online users' social activities, resulting in abundant social relations. Social relations provide an independent source for recommendation, bringing about new opportunities for recommender systems. Exploiting social relations …

Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion

2021

Human-curated knowledge graphs provide critical supportive information to various natural language processing tasks, but these graphs are usually incomplete, urging auto-completion of them (a.k.a. knowledge graph completion). Prevalent graph embedding approaches, e.g., TransE, learn structured …

A Survey on Evaluation of Large Language Models

2024 · ACM Transactions on Intelligent Systems and Technology

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, …