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.


2024

Date Title Speaker
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

2023

Date Title Speaker
12. 27, 2023 SGCL: Contrastive Representation Learning for Signed Graphs Kahyun Park
12. 06, 2023 A scalable optimization approach for fitting canonical tensor decompositions Jeongyoung Lee
11. 29, 2023 Chasing Higher FLOPS for Faster Neural Networks Yong-chan Park
11. 22, 2023 SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models Sojin Lee
11. 15, 2023 Mastering Stock Markets with Efficient Mixture of Diversified Trading Experts Jihyeong Jeon
11. 08, 2023 MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System Jin-gee Kim
11. 01, 2023 Powerful Graph Convolutioal Networks with Adaptive Propagation Mechanism for Homophily and Heterophily Junghun Kim
10. 25, 2023 Knowledge Graph Contrastive Learning for Recommendation Jongjin Kim
10. 11, 2023 Translating Embeddings for Modeling Multi-relational Data Hoyoung Yoon
10. 04, 2023 Enhancing Stock Movement Prediction with Adversarial Training Saurav Tanwar
09. 27, 2023 Contrastive Learning for Sequential Recommendation by Xu Xie et al. (SIGIR’21) Jeongin Yun
09. 20, 2023 Fast and Accurate Transferability Measurement by Evaluating Intra-class Feature Variance Huiwen Xu
09. 13, 2023 SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot Seungcheol Park
09. 06, 2023 Are Transformers Effective for Time Series Forecasting? Taehun Kim
08. 23, 2023 Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States Jiwon Park
08. 16, 2023 Are Graph Augmentations Necessary?: Simple Graph Contrastive Learning for Recommendation Jong-eun Lee
08. 09, 2023 Teach Less, Learn More: On the Undistillable Classes in Knowledge Distillation Jaehyeon Choi
07. 26, 2023 Deep Reinforcement Learning from Human Preferences Chanhee Park
07. 19, 2023 Individualized Indicator for All: Stock-wise Technical Indicator Optimization with Stock Embedding Yejun Soun
07. 12, 2023 Multi-Behavior Recommendation with Cascading Graph Convolution Networks Doyeon Kim
07. 05, 2023 Signed Graph Attention Networks Kahyun Park
06. 28, 2023 Global Filter Networks for Image Classification Yong-chan Park
06. 21, 2023 Application of Deep Q-Network in Portfolio Management Jin-gee Kim
06. 07, 2023 SPADE: Streaming PARAFAC2 Decomposition for Large Datasets Jeongyoung Lee
05. 31, 2023 MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization Jihyeong Jeon
05. 17, 2023 Predict then Propagate: Graph Neural Networks meet Personalized PageRank Junghun Kim
05. 10, 2023 Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation Jongjin Kim
05. 03, 2023 KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation Hoyoung Yoon
04. 26, 2023 Aggregately Diversified Bundle Recommendation via Popularity Debiasing and Configuration-aware Reranking Hyunsik Jeon
04. 12, 2023 Stock Price Prediction Using News Sentiment Analysis Saurav Tanwar
04. 05, 2023 GSL4Rec: Session-based Recommendations with Collective Graph Structure Learning and Next Interaction Prediction Jeongin Yun
03. 29, 2023 Graph Heterogeneous Multi-Relational Recommendation Jaeri Lee
03. 22, 2023 Rabbit Holes and Taste Distortion: Distribution-Aware Recommendation with Evolving Interests Jongjin Kim
03. 15, 2023 FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling Huiwen Xu
03. 08, 2023 A Fast Post-Training Pruning Framework for Transformers Seungcheol Park
02. 27, 2023 IOT: Instance-wise Layer Reordering for Transformer Structures Hyojin Jeon
02. 20, 2023 CuCo: Graph Representation with Curriculum Contrastive Learning Sooyeon Shim
02. 13, 2023 SETN: Stock Embedding Enhanced with Textual and Network Information Jaemin Park
02. 06, 2023 NPA: Neural News Recommendation with Personalized Attention Jiyun Kim
01. 30, 2023 A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction Taehun Kim
01. 09, 2023 DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation Jiwon Park
01. 02, 2023 LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation Jong-eun Lee