22 citations · 70 across the 16 of their papers we have counts for
9 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…
Training-Free Uncertainty Estimation for Embedding Models
Young-Jin Park, Hao Wang, Addison Kristanto Julistiono +2
Embedding models, often obtained via self-supervised learning, extract general-purpose representations from data. Quantifying the reliability of these representations is crucial, a…
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…
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…
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…
Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning
Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae +2
We present a hierarchical planning and control framework that enables an agent to perform various tasks and adapt to a new task flexibly. Rather than learning an individual policy…
div2vec: Diversity-Emphasized Node Embedding
Jisu Jeong, Jeong-Min Yun, Hongi Keam +3
Recently, the interest of graph representation learning has been rapidly increasing in recommender systems. However, most existing studies have focused on improving accuracy, but i…
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…
Adaptive Path-Integral Autoencoder: Representation Learning and Planning for Dynamical Systems
Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae +2
We present a representation learning algorithm that learns a low-dimensional latent dynamical system from high-dimensional \textit{sequential} raw data, e.g., video. The framework…