39 citations · 124 across the 18 of their papers we have counts for
11 papers · 1 filter
A Scalable and Transferable Time Series Prediction Framework for Demand Forecasting
Young-Jin Park, Donghyun Kim, Frédéric Odermatt +2
Time series forecasting is one of the most essential and ubiquitous tasks in many business problems, including demand forecasting and logistics optimization. Traditional time serie…
Direct Preference-based Policy Optimization without Reward Modeling
Gaon An, Junhyeok Lee, Xingdong Zuo +3
Preference-based reinforcement learning (PbRL) is an approach that enables RL agents to learn from preference, which is particularly useful when formulating a reward function is ch…
Deformable Graph Transformer
Jinyoung Park, Seongjun Yun, Hyeonjin Park +5
Transformer-based models have recently shown success in representation learning on graph-structured data beyond natural language processing and computer vision. However, the succes…
VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting
Kashif Rasul, Young-Jin Park, Max Nihlén Ramström +1
Time series models aim for accurate predictions of the future given the past, where the forecasts are used for important downstream tasks like business decision making. In practice…
Hazard Gradient Penalty for Survival Analysis
Seungjae Jung, Kyung-Min Kim
Survival analysis appears in various fields such as medicine, economics, engineering, and business. Recent studies showed that the Ordinary Differential Equation (ODE) modeling fra…
Metropolis-Hastings Data Augmentation for Graph Neural Networks
Hyeonjin Park, Seunghun Lee, Sihyeon Kim +5
Graph Neural Networks (GNNs) often suffer from weak-generalization due to sparsely labeled data despite their promising results on various graph-based tasks. Data augmentation is a…
Global-Local Item Embedding for Temporal Set Prediction
Seungjae Jung, Young-Jin Park, Jisu Jeong +4
Temporal set prediction is becoming increasingly important as many companies employ recommender systems in their online businesses, e.g., personalized purchase prediction of shoppi…
Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning
Dasol Hwang, Jinyoung Park, Sunyoung Kwon +3
In recent years, graph neural networks (GNNs) have been widely adopted in the representation learning of graph-structured data and provided state-of-the-art performance in various…
A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting
Seungjae Jung, Kyung-Min Kim, Hanock Kwak +1
Probabilistic time-series models become popular in the forecasting field as they help to make optimal decisions under uncertainty. Despite the growing interest, a lack of thorough…
Multi-Manifold Learning for Large-scale Targeted Advertising System
Kyuyong Shin, Young-Jin Park, Kyung-Min Kim +1
Messenger advertisements (ads) give direct and personal user experience yielding high conversion rates and sales. However, people are skeptical about ads and sometimes perceive the…
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs
Dasol Hwang, Jinyoung Park, Sunyoung Kwon +3
Graph neural networks have shown superior performance in a wide range of applications providing a powerful representation of graph-structured data. Recent works show that the repre…