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Kuang-Da Wang

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6 papers

1 author row

AAAI Conference 2025 Conference Paper

APAR: Modeling Irregular Target Functions in Tabular Regression via Arithmetic-Aware Pre-Training and Adaptive-Regularized Fine-Tuning

Hong-Wei Wu
Wei-Yao Wang
Kuang-Da Wang
Wen-Chih Peng

Tabular data are fundamental in common machine learning applications, ranging from finance to genomics and healthcare. This paper focuses on tabular regression tasks, a field where deep learning (DL) methods are not consistently superior to machine learning (ML) models due to the challenges posed by irregular target functions inherent in tabular data, causing sensitive label changes with minor variations from features. To address these issues, we propose a novel Arithmetic-Aware Pre-training and Adaptive-Regularized Fine-tuning framework (APAR), which enables the model to fit irregular target function in tabular data while reducing the negative impact of overfitting. In the pre-training phase, APAR introduces an arithmetic-aware pretext objective to capture intricate sample-wise relationships from the perspective of continuous labels. In the fine-tuning phase, a consistency-based adaptive regularization technique is proposed to self-learn appropriate data augmentation. Extensive experiments across 10 datasets demonstrated that APAR outperforms existing GBDT-, supervised NN-, and pretrain-finetune NN-based methods in RMSE (+9.43% ~ 20.37%), and empirically validated the effects of pre-training tasks, including the study of arithmetic operations.

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AAMAS Conference 2025 Conference Paper

RallyDiffuser: A Representation-Guided Diffusion Model Framework for Strategic Planning in Badminton

Bing-Zhi Ke
Kuang-Da Wang
Wen-Chih Peng

The rising interest in sports analysis has led to many studies from various perspectives, such as strategic insights and behavior prediction. In the rapid tactic nature of turn-based sports, badminton stands out as a compelling example of a game requiring players to make strategy-oriented decisions. Exiting planning works fail to capture its complex decision-making dynamics, particularly in balancing long-term strategy execution with immediate scoring opportunities in a turn-based setting. In this work, we propose RallyDiffuser, an innovative representation-guided diffusion model for strategic planning in badminton. We build a strategy latent space through representation learning that captures the variations in player strategies executed during rallies, and it identifies strategic anchors that guide agents in balancing long-term strategic objectives with short-term scoring opportunities. Our experiments demonstrate that RallyDiffuser outperforms existing planning methods, emerging as the only approach that achieves improved win rates across all strategies.

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AAAI Conference 2024 Conference Paper

Root Cause Analysis in Microservice Using Neural Granger Causal Discovery

Cheng-Ming Lin
Ching Chang
Wei-Yao Wang
Kuang-Da Wang
Wen-Chih Peng

In recent years, microservices have gained widespread adoption in IT operations due to their scalability, maintenance, and flexibility. However, it becomes challenging for site reliability engineers (SREs) to pinpoint the root cause due to the complex relationship in microservices when facing system malfunctions. Previous research employed structure learning methods (e.g., PC-algorithm) to establish causal relationships and derive root causes from causal graphs. Nevertheless, they ignored the temporal order of time series data and failed to leverage the rich information inherent in the temporal relationships. For instance, in cases where there is a sudden spike in CPU utilization, it can lead to an increase in latency for other microservices. However, in this scenario, the anomaly in CPU utilization occurs before the latency increases, rather than simultaneously. As a result, the PC-algorithm fails to capture such characteristics. To address these challenges, we propose RUN, a novel approach for root cause analysis using neural Granger causal discovery with contrastive learning. RUN enhances the backbone encoder by integrating contextual information from time series and leverages a time series forecasting model to conduct neural Granger causal discovery. In addition, RUN incorporates Pagerank with a personalization vector to efficiently recommend the top-k root causes. Extensive experiments conducted on the synthetic and real-world microservice-based datasets demonstrate that RUN noticeably outperforms the state-of-the-art root cause analysis methods. Moreover, we provide an analysis scenario for the sock-shop case to showcase the practicality and efficacy of RUN in microservice-based applications. Our code is publicly available at https://github.com/zmlin1998/RUN.

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AAAI Conference 2024 Short Paper

The CoachAI Badminton Environment: A Novel Reinforcement Learning Environment with Realistic Opponents (Student Abstract)

Kuang-Da Wang
Wei-Yao Wang
Yu-Tse Chen
Yu-Heng Lin
Wen-Chih Peng

The growing demand for precise sports analysis has been explored to improve athlete performance in various sports (e.g., basketball, soccer). However, existing methods for different sports face challenges in validating strategies in environments due to simple rule-based opponents leading to performance gaps when deployed in real-world matches. In this paper, we propose the CoachAI Badminton Environment, a novel reinforcement learning (RL) environment with realistic opponents for badminton, which serves as a compelling example of a turn-based game. It supports researchers in exploring various RL algorithms with the badminton context by integrating state-of-the-art tactical-forecasting models and real badminton game records. The Badminton Benchmarks are proposed with multiple widely adopted RL algorithms to benchmark the performance of simulating matches against real players. To advance novel algorithms and developments in badminton analytics, we make our environment open-source, enabling researchers to simulate more complex badminton sports scenarios based on this foundation. Our code is available at https://github.com/wywyWang/CoachAI-Projects/tree/main/CoachAI%20Badminton%20Environment.

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AAAI Conference 2024 System Paper

The CoachAI Badminton Environment: Bridging the Gap between a Reinforcement Learning Environment and Real-World Badminton Games

Kuang-Da Wang
Yu-Tse Chen
Yu-Heng Lin
Wei-Yao Wang
Wen-Chih Peng

We present the CoachAI Badminton Environment, a reinforcement learning (RL) environment tailored for AI-driven sports analytics. In contrast to traditional environments using rule-based opponents or simplistic physics-based randomness, our environment integrates authentic opponent AIs and realistic randomness derived from real-world matches data to bridge the performance gap encountered in real-game deployments. This novel feature enables RL agents to seamlessly adapt to genuine scenarios. The CoachAI Badminton Environment empowers researchers to validate strategies in intricate real-world settings, offering: i) Realistic opponent simulation for RL training; ii) Visualizations for evaluation; and iii) Performance benchmarks for assessing agent capabilities. By bridging the RL environment with actual badminton games, our environment is able to advance the discovery of winning strategies for players. Our code is available at https://github.com/wywyWang/CoachAI-Projects/tree/main/Strategic%20Environment.

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AAAI Conference 2023 Short Paper

A Reinforcement Learning Badminton Environment for Simulating Player Tactics (Student Abstract)

Li-Chun Huang
Nai-Zen Hsueh
Yen-Che Chien
Wei-Yao Wang
Kuang-Da Wang
Wen-Chih Peng

Recent techniques for analyzing sports precisely has stimulated various approaches to improve player performance and fan engagement. However, existing approaches are only able to evaluate offline performance since testing in real-time matches requires exhaustive costs and cannot be replicated. To test in a safe and reproducible simulator, we focus on turn-based sports and introduce a badminton environment by simulating rallies with different angles of view and designing the states, actions, and training procedures. This benefits not only coaches and players by simulating past matches for tactic investigation, but also researchers from rapidly evaluating their novel algorithms. Our code is available at https://github.com/wywyWang/CoachAI-Projects/tree/main/Strategic%20Environment.

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