https://scholarworks.unist.ac.kr/handle/201301/45 2026-04-08T00:28:56Z https://scholarworks.unist.ac.kr/handle/201301/90488 Title: Neural Differential Equations for Continuous-Time Analysis Author(s): Oh, Yongkyung; Lim, Dongyoung; Kim, Sungil Abstract: Modeling complex, irregular time series is a critical challenge in knowledge discovery and data mining. This tutorial introduces Neural Differential Equations (NDEs) - a powerful paradigm for continuous-time deep learning that intrinsically handles the non-uniform sampling and missing values where traditional models falter. We provide a comprehensive review of the theory and practical application of the entire NDE family: Neural Ordinary (NODEs), Controlled (NCDEs), and Stochastic (NSDEs) Differential Equations. The tutorial emphasizes robustness and stability and culminates in a hands-on session where participants will use key open-source libraries to solve real-world tasks like interpolation and classification. Designed for AI researchers and practitioners, this tutorial equips attendees with essential tools for time series analysis. 2025-11-09T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/89424 Title: Geodesic Flow Kernels for Semi-Supervised Learning on Mixed-Variable Tabular Dataset Author(s): Hwang, Yoontae; Lee, Yongjae Abstract: Tabular data poses unique challenges due to its heterogeneous nature, combining both continuous and categorical variables. Existing approaches often struggle to effectively capture the underlying structure and relationships within such data. We propose GFTab (Geodesic Flow Kernels for Semi-Supervised Learning on Mixed-Variable Tabular Dataset), a semi-supervised framework specifically designed for tabular datasets. GFTab incorporates three key innovations: 1) Variable-specific corruption methods tailored to the distinct properties of continuous and categorical variables, 2) A Geodesic flow kernel based similarity measure to capture geometric changes between corrupted inputs, and 3) Tree-based embedding to leverage hierarchical relationships from available labeled data. To rigorously evaluate GFTab, we curate a comprehensive set of 21 tabular datasets spanning various domains, sizes, and variable compositions. Our experimental results show that GFTab outperforms existing ML/DL models across many of these datasets, particularly in settings with limited labeled data. 2025-04-10T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/89422 Title: Information Retrieval in Finance: Industry and Academic Perspectives on Innovation Author(s): Chen, Chung-Chi; Lee, Yongjae; Lopez-Lira, Alejandro; Choi, Chanyeol; McCreadie, Richard; Sanz-Cruzado, Javier Abstract: Information retrieval (IR) plays a critical role in financial decision-making across investment research, trading, risk management, and reporting. With the rise of large language models (LLMs), IR systems have evolved to support more natural, context-aware workflows. In this tutorial, we survey recent advances in applying IR and LLM technologies in finance, covering agent-based simulations, investor recommender systems, retrieval-augmented research management, and LLM-driven portfolio construction. We highlight practical challenges and propose future research directions at the intersection of IR, LLMs, and financial innovation. More materials can be found at http://irfin.nlpfin.com/. 2025-07-12T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/89415 Title: THEME: Enhancing Thematic Investing with Semantic Stock Representations and Temporal Dynamics Author(s): Lee, Hoyoung; Ahn, Wonbin; Park, Suhwan; Lee, Jaehoon; Kim, Minjae; Yoo, Sungdong; Lim, Taeyoon; Lim, Woohyung; Lee, Yongjae Abstract: Thematic investing, which aims to construct portfolios aligned with structural trends, remains a challenging endeavor due to overlapping sector boundaries and evolving market dynamics. A promising direction is to build semantic representations of investment themes from textual data. However, despite their power, general-purpose LLM embedding models are not well-suited to capture the nuanced characteristics of financial assets, since the semantic representation of investment assets may differ fundamentally from that of general financial text. To address this, we introduce THEME, a framework that fine-tunes embeddings using hierarchical contrastive learning. THEME aligns themes and their constituent stocks using their hierarchical relationship, and subsequently refines these embeddings by incorporating stock returns. This process yields representations effective for retrieving thematically aligned assets with strong return potential. Empirical results demonstrate that THEME excels in two key areas. For thematic asset retrieval, it significantly outperforms leading large language models. Furthermore, its constructed portfolios demonstrate compelling performance. By jointly modeling thematic relationships from text and market dynamics from returns, THEME generates stock embeddings specifically tailored for a wide range of practical investment applications. 2025-11-09T15:00:00Z