Author name cluster

Woohyung Lim

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13 papers

2 author rows

AAAI Conference 2026 System Paper

AEGIS: Toward Expert-in-the-loop Industrial Anomaly Detection

Dongmin Kim
Ye Seul Sim
Suhee Yoon
Sanghyu Yoon
Seungdong Yoa
Soonyoung Lee
Woohyung Lim

Anomaly detection platforms in real-world environments require continuous interaction between automated systems and domain experts, as anomalies evolve dynamically and their definitions vary across contexts. Therefore, an effective platform must collaborate with experts and incorporate their feedback to update the system. This paper introduces AEGIS, an anomaly detection platform that aims to support interaction between domain experts and data-driven agents through three core capabilities: (1) data-driven insights through real-time monitoring, explanations, and distribution shift detection, which invoke customized tools to generate appropriate responses, (2) an expert feedback interface for labeling and direct updates via chat-based interaction, and (3) autonomous model construction that leverages expert-labeled data with LLM-driven hyperparameter optimization. Through this design, AEGIS fosters continuous interaction in which the platform provides insights while experts guide model improvement, ensuring user intent is reflected and robustness is maintained under evolving data distributions.

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AAAI Conference 2026 System Paper

OrcheCause Agent: From Textual Knowledge to End-to-End Causal Inference

Jinseok Yang
Jung-Hee Kim
Juhyun Lyu
Soonyoung Lee
Woohyung Lim

Causal agents have emerged as promising tools for automating causal analysis based on user queries. However, existing causal agent systems are often limited to a single causal task, limiting their ability to handle complex queries. In addition, they accept only numerical data as input, preventing the integration of domain knowledge expressed in natural language. To overcome these limitations, we propose the OrcheCause agent, a causal agent leveraging textual knowledge for end-to-end causal inference. Specifically, OrcheCause is designed to orchestrate a sequence of interrelated causal tasks in response to user queries. Furthermore, OrcheCause supports diverse data types—numerical as well as textual data—by extracting cause-effect pairs from the relevant sources and incorporating them into causal discovery (CD), thereby improving the performance of CD. OrcheCause also introduces a metric-based hyperparameter optimization framework for CD when ground-truth graphs are not available.

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AAAI Conference 2026 System Paper

RAPID: A Rapid Prototyping Platform for Industrial Automation

Sunghoon Hong
Junseok Park
Whiyoung Jung
Deunsol Yoon
Woohyung Lim
Soonyoung Lee
Kanghoon Lee

Industrial automation in smart logistics and factories requires simulation platforms that support rapid environment building before costly physical deployment. Yet existing tools often require substantial expertise, complex setup, and long configuration times, hindering agile prototyping. We present RAPID, a simulation platform with two components: layout design, which enables intuitive visual configuration of factory layouts, and behavior simulation and validation, which allows users to attach behavior models and evaluate system performance. RAPID lowers the entry barrier to industrial simulation, letting users apply existing behavior models or trained reinforcement learning (RL) agents to new layouts with minimal effort. This approach lets practitioners prototype facilities in minutes rather than weeks and gives researchers a standardized environment for benchmarking multi-agent RL and coordination algorithms. By combining rapid design with simulation-based validation, RAPID accelerates automation development from concept to implementation.

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AAAI Conference 2026 System Paper

RL-Studio: A System for Multi-Phase Reinforcement Learning Experimentation

Whiyoung Jung
Sunghoon Hong
Deunsol Yoon
Jeonghye Kim
Yongjae Shin
Suhyun Jung
Hyundam Yoo
Youngjin Kim

Reinforcement learning (RL) has evolved beyond monolithic training, yet existing frameworks remain limited to single algorithms or simple offline-to-online transitions. We present multi-phase RL, a framework that orchestrates multiple learning phases for continual policy improvement. It enables efficient fine-tuning of pretrained policies with new data and smooth adaptation from simulation to real-world environments. To support this paradigm, we introduce RL-Studio, a platform that addresses key implementation barriers, including neural architecture mismatches, parameter transfer complexities, and experiment management overhead. It provides phase orchestration, transition-point monitoring, and full experiment lineage tracking. We demonstrate the effectiveness of multi-phase RL through representative scenarios and highlight RL-Studio’s capabilities.

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ICML Conference 2025 Conference Paper

Agent-Centric Actor-Critic for Asynchronous Multi-Agent Reinforcement Learning

Whiyoung Jung
Sunghoon Hong
Deunsol Yoon
Kanghoon Lee
Woohyung Lim

Multi-Agent Reinforcement Learning (MARL) struggles with coordination in sparse reward environments. Macro-actions —sequences of actions executed as single decisions— facilitate long-term planning but introduce asynchrony, complicating Centralized Training with Decentralized Execution (CTDE). Existing CTDE methods use padding to handle asynchrony, risking misaligned asynchronous experiences and spurious correlations. We propose the Agent-Centric Actor-Critic (ACAC) algorithm to manage asynchrony without padding. ACAC uses agent-centric encoders for independent trajectory processing, with an attention-based aggregation module integrating these histories into a centralized critic for improved temporal abstractions. The proposed structure is trained via a PPO-based algorithm with a modified Generalized Advantage Estimation for asynchronous environments. Experiments show ACAC accelerates convergence and enhances performance over baselines in complex MARL tasks.

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AAAI Conference 2025 Conference Paper

Diffusion-based Semantic Outlier Generation via Nuisance Awareness for Out-of-Distribution Detection

Suhee Yoon
Sanghyu Yoon
Ye Seul Sim
Sungik Choi
Kyungeun Lee
Hye-Seung Cho
Hankook Lee
Woohyung Lim

Out-of-distribution (OOD) detection, determining whether a given sample is part of the in-distribution (ID) or not, has been newly explored by a generative model-based outlier synthesizing approach, especially with diffusion models. Nonetheless, existing diffusion models often produce outliers that are considerably distant from the ID in pixel-space, showing limited efficacy for capturing subtle distinctions between ID and OOD. To address these issues, we propose a novel framework, Semantic Outlier generation via Nuisance Awareness (SONA), which directly utilizes informative pixel-space ID images in diffusion models. Thereby, the generated outliers achieve two crucial properties: (i) they closely resemble the ID mainly in nuisances, while (ii) represent discriminative semantic information. To facilitate the separate effect on semantics and nuisances, we introduce SONA guidance, providing region-specific guidance. Extensive experiments demonstrate the effectiveness of our framework, achieving an impressive AUROC of 87% on near-OOD datasets, which surpasses the performance of baseline methods by a significant margin of approximately 6%.

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AAAI Conference 2025 Conference Paper

ImagePiece: Content-aware Re-tokenization for Efficient Image Recognition

Seungdong Yoa
Seungjun Lee
Hye-Seung Cho
Bumsoo Kim
Woohyung Lim

Vision Transformers (ViTs) have achieved remarkable success in various computer vision tasks. However, ViTs have a huge computational cost due to their inherent reliance on multi-head self-attention (MHSA), prompting efforts to accelerate ViTs for practical applications. To this end, recent works aim to reduce the number of tokens, mainly focusing on how to effectively prune or merge them. Nevertheless, since ViT tokens are generated from non-overlapping grid patches, they usually do not convey sufficient semantics, making it incompatible with efficient ViTs. To address this, we propose ImagePiece, a novel re-tokenization strategy for Vision Transformers. Following the MaxMatch strategy of NLP tokenization, ImagePiece groups semantically insufficient yet locally coherent tokens until they convey meaning. This simple retokenization is highly compatible with previous token reduction methods, being able to drastically narrow down relevant tokens, enhancing the inference speed of DeiT-S by 54% (nearly 1.5x faster) while achieving a 0.39% improvement in ImageNet classification accuracy. For hyper-speed inference scenarios (with 251% acceleration), our approach surpasses other baselines by an accuracy over 8%.

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ICML Conference 2025 Conference Paper

Online Pre-Training for Offline-to-Online Reinforcement Learning

Yongjae Shin
Jeonghye Kim
Whiyoung Jung
Sunghoon Hong
Deunsol Yoon
Youngsoo Jang
Geon-Hyeong Kim
Jongseong Chae

Offline-to-online reinforcement learning (RL) aims to integrate the complementary strengths of offline and online RL by pre-training an agent offline and subsequently fine-tuning it through online interactions. However, recent studies reveal that offline pre-trained agents often underperform during online fine-tuning due to inaccurate value estimation caused by distribution shift, with random initialization proving more effective in certain cases. In this work, we propose a novel method, Online Pre-Training for Offline-to-Online RL (OPT), explicitly designed to address the issue of inaccurate value estimation in offline pre-trained agents. OPT introduces a new learning phase, Online Pre-Training, which allows the training of a new value function tailored specifically for effective online fine-tuning. Implementation of OPT on TD3 and SPOT demonstrates an average 30% improvement in performance across a wide range of D4RL environments, including MuJoCo, Antmaze, and Adroit.

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ICML Conference 2025 Conference Paper

Penalizing Infeasible Actions and Reward Scaling in Reinforcement Learning with Offline Data

Jeonghye Kim
Yongjae Shin
Whiyoung Jung
Sunghoon Hong
Deunsol Yoon
Youngchul Sung
Kanghoon Lee
Woohyung Lim

Reinforcement learning with offline data suffers from Q-value extrapolation errors. To address this issue, we first demonstrate that linear extrapolation of the Q-function beyond the data range is particularly problematic. To mitigate this, we propose guiding the gradual decrease of Q-values outside the data range, which is achieved through reward scaling with layer normalization (RS-LN) and a penalization mechanism for infeasible actions (PA). By combining RS-LN and PA, we develop a new algorithm called PARS. We evaluate PARS across a range of tasks, demonstrating superior performance compared to state-of-the-art algorithms in both offline training and online fine-tuning on the D4RL benchmark, with notable success in the challenging AntMaze Ultra task.

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AAAI Conference 2025 Conference Paper

Representation Space Augmentation for Effective Self-Supervised Learning on Tabular Data

Moonjung Eo
Kyungeun Lee
Hye-Seung Cho
Dongmin Kim
Ye Seul Sim
Woohyung Lim

Tabular data, widely used across industries, remains underexplored in deep learning. Self-supervised learning (SSL) shows promise for pre-training deep neural networks (DNNs) on tabular data, but its potential is hindered by challenges in designing suitable augmentations. Unlike image and text data, where SSL leverages inherent spatial or semantic structures, tabular data lacks such explicit structure. This makes traditional input-level augmentations, like modifying or removing features, less effective due to difficulties in balancing critical information preservation with variability. To address these challenges, we propose RaTab, a novel method that shifts augmentation from input-level to representation-level using matrix factorization, specifically truncated SVD. This approach preserves essential data structures while generating diverse representations by applying dropout at various stages of the representation, thereby significantly enhancing SSL performance for tabular data.

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ICML Conference 2024 Conference Paper

Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains

Kyungeun Lee
Ye Seul Sim
Hye-Seung Cho
Moonjung Eo
Suhee Yoon
Sanghyu Yoon
Woohyung Lim

The ability of deep networks to learn superior representations hinges on leveraging the proper inductive biases, considering the inherent properties of datasets. In tabular domains, it is critical to effectively handle heterogeneous features (both categorical and numerical) in a unified manner and to grasp irregular functions like piecewise constant functions. To address the challenges in the self-supervised learning framework, we propose a novel pretext task based on the classical binning method. The idea is straightforward: reconstructing the bin indices (either orders or classes) rather than the original values. This pretext task provides the encoder with an inductive bias to capture the irregular dependencies, mapping from continuous inputs to discretized bins, and mitigates the feature heterogeneity by setting all features to have category-type targets. Our empirical investigations ascertain several advantages of binning: capturing the irregular function, compatibility with encoder architecture and additional modifications, standardizing all features into equal sets, grouping similar values within a feature, and providing ordering information. Comprehensive evaluations across diverse tabular datasets corroborate that our method consistently improves tabular representation learning performance for a wide range of downstream tasks. The codes are available in https: //github. com/kyungeun-lee/tabularbinning.

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ICLR Conference 2024 Conference Paper

Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Tasks

Sung Moon Ko
Sumin Lee
Dae-Woong Jeong
Woohyung Lim
Sehui Han

Transfer learning is a crucial technique for handling a small amount of data that is potentially related to other abundant data. However, most of the existing methods are focused on classification tasks using images and language datasets. Therefore, in order to expand the transfer learning scheme to regression tasks, we propose a novel transfer technique based on differential geometry, namely the Geometrically Aligned Transfer Encoder (${\it GATE}$). In this method, we interpret the latent vectors from the model to exist on a Riemannian curved manifold. We find a proper diffeomorphism between pairs of tasks to ensure that every arbitrary point maps to a locally flat coordinate in the overlapping region, allowing the transfer of knowledge from the source to the target data. This also serves as an effective regularizer for the model to behave in extrapolation regions. In this article, we demonstrate that ${\it GATE}$ outperforms conventional methods and exhibits stable behavior in both the latent space and extrapolation regions for various molecular graph datasets.

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AAMAS Conference 2024 Conference Paper

Naphtha Cracking Center Scheduling Optimization using Multi-Agent Reinforcement Learning

Sunghoon Hong
Deunsol Yoon
Whiyoung Jung
Jinsang Lee
Hyundam Yoo
Jiwon Ham
Suhyun Jung
Chanwoo Moon

The Naphtha Cracking Center (NCC) is central to petrochemical feedstock production through the intricate process. It consists of receipt stage for unloading naphtha, blending stage for mixing naphtha, and furnace stage for producing marketable products. It is crucial to make an optimal schedule for NCC for profitability and efficiency. Traditionally managed by human experts, challenges arise in predicting complex chemical reactions and navigating real-world complexities. To address these issues, this paper aims to develop autonomous NCC operation using multi-agent reinforcement learning, where each agent is responsible for each stage and collaborates to achieve common objectives, while adhering to real-world constraints. We developed an online web service to allow the staff in LG Chem Daesan NCC facility to obtain an NCC schedule in real-time, and the staff are now operating the facility based on the schedules generated by the online web service.

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