Search Results for author: Kwei-Herng Lai

Found 21 papers, 13 papers with code

DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research

1 code implementation4 Sep 2023 Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Kwei-Herng Lai, Daochen Zha, Ruixiang Tang, Fan Yang, Alfredo Costilla Reyes, Kaixiong Zhou, Xiaoqian Jiang, Xia Hu

The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research.

named-entity-recognition Named Entity Recognition +4

Hessian-aware Quantized Node Embeddings for Recommendation

no code implementations2 Sep 2023 Huiyuan Chen, Kaixiong Zhou, Kwei-Herng Lai, Chin-Chia Michael Yeh, Yan Zheng, Xia Hu, Hao Yang

To address the gradient mismatch problem in STE, we further consider the quantized errors and its second-order derivatives for better stability.

Recommendation Systems Retrieval

Tackling Diverse Minorities in Imbalanced Classification

no code implementations28 Aug 2023 Kwei-Herng Lai, Daochen Zha, Huiyuan Chen, Mangesh Bendre, Yuzhong Chen, Mahashweta Das, Hao Yang, Xia Hu

Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers.

Anomaly Detection Classification +2

Towards Assumption-free Bias Mitigation

no code implementations9 Jul 2023 Chia-Yuan Chang, Yu-Neng Chuang, Kwei-Herng Lai, Xiaotian Han, Xia Hu, Na Zou

Those studies face challenges, either in inaccurate predictions of sensitive attributes or the need to mitigate unequal distribution of manually defined non-sensitive attributes related to bias.

Context-aware Domain Adaptation for Time Series Anomaly Detection

no code implementations15 Apr 2023 Kwei-Herng Lai, Lan Wang, Huiyuan Chen, Kaixiong Zhou, Fei Wang, Hao Yang, Xia Hu

We formulate context sampling into the Markov decision process and exploit deep reinforcement learning to optimize the time series domain adaptation process via context sampling and design a tailored reward function to generate domain-invariant features that better align two domains for anomaly detection.

Anomaly Detection Domain Adaptation +3

Data-centric Artificial Intelligence: A Survey

10 code implementations17 Mar 2023 Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, Zhimeng Jiang, Shaochen Zhong, Xia Hu

Artificial Intelligence (AI) is making a profound impact in almost every domain.

Data-centric AI: Perspectives and Challenges

1 code implementation12 Jan 2023 Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, Xia Hu

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 advancements to ensuring data quality and reliability.

Mitigating Relational Bias on Knowledge Graphs

no code implementations26 Nov 2022 Yu-Neng Chuang, Kwei-Herng Lai, Ruixiang Tang, Mengnan Du, Chia-Yuan Chang, Na Zou, Xia Hu

Knowledge graph data are prevalent in real-world applications, and knowledge graph neural networks (KGNNs) are essential techniques for knowledge graph representation learning.

Graph Representation Learning Knowledge Graphs +1

DreamShard: Generalizable Embedding Table Placement for Recommender Systems

1 code implementation5 Oct 2022 Daochen Zha, Louis Feng, Qiaoyu Tan, Zirui Liu, Kwei-Herng Lai, Bhargav Bhushanam, Yuandong Tian, Arun Kejariwal, Xia Hu

Although prior work has explored learning-based approaches for the device placement of computational graphs, embedding table placement remains to be a challenging problem because of 1) the operation fusion of embedding tables, and 2) the generalizability requirement on unseen placement tasks with different numbers of tables and/or devices.

Recommendation Systems Reinforcement Learning (RL)

Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning

2 code implementations26 Aug 2022 Daochen Zha, Kwei-Herng Lai, Qiaoyu Tan, Sirui Ding, Na Zou, Xia Hu

Motivated by this, we investigate developing a learning-based over-sampling algorithm to optimize the classification performance, which is a challenging task because of the huge and hierarchical decision space.

Hierarchical Reinforcement Learning reinforcement-learning +1

Towards Similarity-Aware Time-Series Classification

1 code implementation5 Jan 2022 Daochen Zha, Kwei-Herng Lai, Kaixiong Zhou, Xia Hu

Prior work has approached TSC from two major directions: (1) similarity-based methods that classify time-series based on the nearest neighbors, and (2) deep learning models that directly learn the representations for classification in a data-driven manner.

Classification Dynamic Time Warping +5

Simplifying Deep Reinforcement Learning via Self-Supervision

1 code implementation10 Jun 2021 Daochen Zha, Kwei-Herng Lai, Kaixiong Zhou, Xia Hu

Supervised regression to demonstrations has been demonstrated to be a stable way to train deep policy networks.

regression reinforcement-learning +1

Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning

1 code implementation16 Sep 2020 Daochen Zha, Kwei-Herng Lai, Mingyang Wan, Xia Hu

Specifically, existing strategies have been focused on making the top instances more likely to be anomalous based on the feedback.

Anomaly Detection reinforcement-learning +2

Policy-GNN: Aggregation Optimization for Graph Neural Networks

1 code implementation26 Jun 2020 Kwei-Herng Lai, Daochen Zha, Kaixiong Zhou, Xia Hu

It is a challenging task to develop an effective aggregation strategy for each node, given complex graphs and sparse features.

Node Classification Reinforcement Learning (RL)

Dual Policy Distillation

1 code implementation7 Jun 2020 Kwei-Herng Lai, Daochen Zha, Yuening Li, Xia Hu

In this work, we introduce dual policy distillation(DPD), a student-student framework in which two learners operate on the same environment to explore different perspectives of the environment and extract knowledge from each other to enhance their learning.

Continuous Control reinforcement-learning +1

RLCard: A Toolkit for Reinforcement Learning in Card Games

8 code implementations10 Oct 2019 Daochen Zha, Kwei-Herng Lai, Yuanpu Cao, Songyi Huang, Ruzhe Wei, Junyu Guo, Xia Hu

The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with multiple agents, large state and action space, and sparse reward.

Board Games Game of Poker +3

Experience Replay Optimization

no code implementations19 Jun 2019 Daochen Zha, Kwei-Herng Lai, Kaixiong Zhou, Xia Hu

Experience replay enables reinforcement learning agents to memorize and reuse past experiences, just as humans replay memories for the situation at hand.

Continuous Control reinforcement-learning +1

Representation Learning for Image-based Music Recommendation

no code implementations28 Aug 2018 Chih-Chun Hsia, Kwei-Herng Lai, Yi-An Chen, Chuan-Ju Wang, Ming-Feng Tsai

Image perception is one of the most direct ways to provide contextual information about a user concerning his/her surrounding environment; hence images are a suitable proxy for contextual recommendation.

Music Recommendation Representation Learning +1

Superhighway: Bypass Data Sparsity in Cross-Domain CF

no code implementations28 Aug 2018 Kwei-Herng Lai, Ting-Hsiang Wang, Heng-Yu Chi, Yi-An Chen, Ming-Feng Tsai, Chuan-Ju Wang

Cross-domain collaborative filtering (CF) aims to alleviate data sparsity in single-domain CF by leveraging knowledge transferred from related domains.

Collaborative Filtering

Cannot find the paper you are looking for? You can Submit a new open access paper.