Data Mining Seminar

About Seminar

In the data mining seminar, we have talks about various data mining, machine learning, and AI related topics, including data analysis methods, applications, and scalable platforms. The seminar information is as follows.

  • Time: Every Wednesday, 17:30 - 18:30
  • Place: 301-203 / Zoom

The contact information for anything related to the seminar is as follows.

  • Seminar organizer: Jeongin Yun (yji00828@snu.ac.kr)

During the seminar, free pizza is provided to participants.


2025

Date Title Speaker
05. 14, 2025 DART: Diversified and Accurate Long-Tail Recommendation Jeongin Yun
05. 07, 2025 FINCON: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making Jaemin Hong
04. 30, 2025 CoCa: Contrastive Captioners are Image-Text Foundation Models Seung-Ho Kim
04. 09, 2025 GenQ: Quantization in Low Data Regimes with Generative Synthetic Data Jongkeun Lee
03. 26, 2025 SynQ: Accurate Zero-shot Quantization by Synthesis-aware Fine-tuning Minjun Kim
03. 19, 2025 TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting Kisoo Kim
03. 05, 2025 Sequentially Diversified and Accurate Recommendations in Chronological Order for a Series of Users Jongjin Kim
02. 26, 2025 Compact Decomposition of Irregular Tensors for Data Compression: From Sparse to Dense to High-Order Tensors Jeongyoung Lee
02. 19, 2025 Time-Aware Random Walk Diffusion to Improve Dynamic Graph Learning Hoyoung Yun
02. 12, 2025 Accurate Link Prediction for Edge-Incomplete Graphs via PU Learning Junghun Kim
02. 05, 2025 Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction Saurav Tanwar
01. 22, 2025 Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System Jeongin Yun
01. 08, 2025 A Time Series is Worth 64 Words: Long-term Forecasting with Transformers Taehoon Kim

2024

Date Title Speaker
11. 27, 2024 Towards True Multi-interest Recommendation: Enhanced Scheme for Balanced Interest Training Jaeri Lee
11. 20, 2024 DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs Sojin Lee
11. 06, 2024 Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias Jong-eun Lee
10. 30, 2024 DeepCache: Accelerating Diffusion Models for Free Jaehyeon Choi
10. 16, 2024 A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion SeungJoo Lee
10. 02, 2024 Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation Chanhee Park
09. 25, 2024 Modeling Extreme Events in Time Series Prediction Dongho Lee
09. 11, 2024 Coupling Macro-Sector-Micro Financial Indicators for Learning Stock Representations with Less Uncertainty Yejun Soun
09. 04, 2024 Hard Sample Matters a Lot in Zero-Shot Quantization Minjun Kim
08. 21, 2024 FreQuant: A Reinforcement-Learning based Adaptive Portfolio Optimization with Multi-frequency Decomposition Jihyeong Jeon
08. 14, 2024 Proximal Policy Optimization Algorithms Hyunwoo Lee
07. 24, 2024 Fast Multidimensional Partial Fourier Transform with Automatic Hyperparameter Selection Yong-chan Park
07. 17, 2024 Fast and Accurate Domain Adaptation for Irregular Tensor Decomposition Junghun Kim
07. 10, 2024 RSGNN: A Model-agnostic Approach for Enhancing the Robustness of Signed Graph Neural Networks Ka Hyun Park
07. 03, 2024 MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting Kisoo Kim
06. 19, 2024 Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approach Jeongyoung Lee
06. 05, 2024 OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models Sojin Lee
05. 29, 2024 MASTER: Market-Guided Stock Transformer for Stock Price Forecasting Jihyeong Jeon
05. 22, 2024 StockMixer: A Simple Yet Strong MLP-Based Architecture for Stock Price Forecasting Jin-gee Kim
05. 08, 2024 LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation Jongjin Kim
05. 01, 2024 Language Models as Knowledge Embeddings Hoyoung Yoon
04. 24, 2024 Accurate Semi-supervised Automatic Speech Recognition via Multi-hypotheses-based Curriculum Learning Junghun Kim
03. 27, 2024 Co-occurrence Embedding Enhancement for Long-tail Problem in Multi-Interest Recommendation Jeongin Yun
03. 13, 2024 Accurate Retraining-free Pruning for Pretrained Encoder-based Language Models Seungcheol Park
03. 06, 2024 A Transformer-based Framework for Multivariate Time Series Representation Learning Taehun Kim
02. 28, 2024 Select and Trade : Towards Unified Pair Trading with Hierarchical Reinforcement Learning Jiwon Park
02. 21, 2024 How Attentive are Graph Attention Networks? Jong-eun Lee
02. 14, 2024 Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation Jaehyeon Choi
02. 07, 2024 Graph Structure Aware Contrastive Knowledge Distillation for Incremental Learning in Recommender Systems Seungjoo Lee
01. 31, 2024 StockFormer: Learning Hybrid Trading Machines with Predictive Coding Chanhee Park
01. 24, 2024 Investment Behaviors Can Tell What Inside: Exploring Stock Intrinsic Properties for Stock Trend Prediction Yejun Soun
01. 17, 2024 ifMixup: Interpolating Graph Pair to Regularize Graph Classification Minjun Kim
01. 10, 2024 Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport Hyunwoo Lee
01. 03, 2024 Sequential Recommendation with Graph Neural Networks Jaeri Lee