102 citations · 218 across the 26 of their papers we have counts for
28 papers
Learning from Research: Toward Lifelong Agent Harness Evolution
Jingbo Yang, Kwei-Herng Lai, Xiaowen Wang +3
Language agents are expected to solve increasingly complex tasks, creating a growing need for continual improvement. One promising approach is to evolve the agent harness, the soft…
Persistent Context Graphs for Efficient Memory Compaction in LLM Agents
Jingbo Yang, Kwei-Herng Lai, Xiaowen Wang +6
As LLM capabilities advance, agents are tackling increasingly complex tasks over longer horizons. Their growing interaction histories make memory compaction essential for staying w…
A Single Rewrite Suffices: Empirical Lessons from Production Skill Description Optimization
Yangqiaoyu Zhou, Mohammad Alqudah, Kwei-Herng Lai +3
Enterprise AI agents route user queries to specialized skills by matching queries against natural language skill descriptions. When two skills share overlapping descriptions, the r…
GroupMemBench: Benchmarking LLM Agent Memory in Multi-Party Conversations
Jingbo Yang, Kwei-Herng Lai, Xiaowen Wang +3
Large Language Model (LLM) agents increasingly serve as personal assistants and workplace collaborators, where their utility depends on memory systems that extract, retrieve, and a…
Training-Free Time Series Classification via In-Context Reasoning with LLM Agents
Songyuan Sui, Zihang Xu, Xia Hu
Time series classification (TSC) spans diverse application scenarios, yet labeled data are often scarce, making task-specific training costly and inflexible. Recent reasoning-orien…
LTSM-Bundle: A Toolbox and Benchmark on Large Language Models for Time Series Forecasting
Yu-Neng Chuang, Songchen Li, Jiayi Yuan +11
Time Series Forecasting (TSF) has long been a challenge in time series analysis. Inspired by the success of Large Language Models (LLMs), researchers are now developing Large Time…
DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research
Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang +8
The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are…
CODA: Temporal Domain Generalization via Concept Drift Simulator
Chia-Yuan Chang, Yu-Neng Chuang, Zhimeng Jiang +3
In real-world applications, machine learning models often become obsolete due to shifts in the joint distribution arising from underlying temporal trends, a phenomenon known as the…
Hessian-aware Quantized Node Embeddings for Recommendation
Huiyuan Chen, Kaixiong Zhou, Kwei-Herng Lai +4
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in recommender systems. Nevertheless, the process of searching and ranking from a large item corpus usually…
Tackling Diverse Minorities in Imbalanced Classification
Kwei-Herng Lai, Daochen Zha, Huiyuan Chen +5
Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. When working with large datasets, the imbal…
Towards Assumption-free Bias Mitigation
Chia-Yuan Chang, Yu-Neng Chuang, Kwei-Herng Lai +3
Despite the impressive prediction ability, machine learning models show discrimination towards certain demographics and suffer from unfair prediction behaviors. To alleviate the di…
Context-aware Domain Adaptation for Time Series Anomaly Detection
Kwei-Herng Lai, Lan Wang, Huiyuan Chen +4
Time series anomaly detection is a challenging task with a wide range of real-world applications. Due to label sparsity, training a deep anomaly detector often relies on unsupervis…
Data-centric Artificial Intelligence: A Survey
Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai +4
Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler of its great success is the availability of abundant and high-quality data for buil…
Data-centric AI: Perspectives and Challenges
Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai +2
The role of data in building AI systems has recently been significantly magnified by the emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model…
Mitigating Relational Bias on Knowledge Graphs
Yu-Neng Chuang, Kwei-Herng Lai, Ruixiang Tang +4
Knowledge graph data are prevalent in real-world applications, and knowledge graph neural networks (KGNNs) are essential techniques for knowledge graph representation learning. Alt…
DreamShard: Generalizable Embedding Table Placement for Recommender Systems
Daochen Zha, Louis Feng, Qiaoyu Tan +6
We study embedding table placement for distributed recommender systems, which aims to partition and place the tables on multiple hardware devices (e.g., GPUs) to balance the comput…
Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning
Daochen Zha, Kwei-Herng Lai, Qiaoyu Tan +3
Imbalanced learning is a fundamental challenge in data mining, where there is a disproportionate ratio of training samples in each class. Over-sampling is an effective technique to…
Towards Similarity-Aware Time-Series Classification
Daochen Zha, Kwei-Herng Lai, Kaixiong Zhou +1
We study time-series classification (TSC), a fundamental task of time-series data mining. Prior work has approached TSC from two major directions: (1) similarity-based methods that…
AutoVideo: An Automated Video Action Recognition System
Daochen Zha, Zaid Pervaiz Bhat, Yi-Wei Chen +9
Action recognition is an important task for video understanding with broad applications. However, developing an effective action recognition solution often requires extensive engin…
Simplifying Deep Reinforcement Learning via Self-Supervision
Daochen Zha, Kwei-Herng Lai, Kaixiong Zhou +1
Supervised regression to demonstrations has been demonstrated to be a stable way to train deep policy networks. We are motivated to study how we can take full advantage of supervis…
Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning
Daochen Zha, Kwei-Herng Lai, Mingyang Wan +1
High false-positive rate is a long-standing challenge for anomaly detection algorithms, especially in high-stake applications. To identify the true anomalies, in practice, analysts…
TODS: An Automated Time Series Outlier Detection System
Kwei-Herng Lai, Daochen Zha, Guanchu Wang +8
We present TODS, an automated Time Series Outlier Detection System for research and industrial applications. TODS is a highly modular system that supports easy pipeline constructio…
Policy-GNN: Aggregation Optimization for Graph Neural Networks
Kwei-Herng Lai, Daochen Zha, Kaixiong Zhou +1
Graph data are pervasive in many real-world applications. Recently, increasing attention has been paid on graph neural networks (GNNs), which aim to model the local graph structure…
Dual Policy Distillation
Kwei-Herng Lai, Daochen Zha, Yuening Li +1
Policy distillation, which transfers a teacher policy to a student policy has achieved great success in challenging tasks of deep reinforcement learning. This teacher-student frame…