Park, Youngsuk

23 publications

ICML 2025 Enhancing Foundation Models for Time Series Forecasting via Wavelet-Based Tokenization Luca Masserano, Abdul Fatir Ansari, Boran Han, Xiyuan Zhang, Christos Faloutsos, Michael W. Mahoney, Andrew Gordon Wilson, Youngsuk Park, Syama Sundar Rangapuram, Danielle C. Maddix, Bernie Wang

ICML 2025 Proxsparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Hongyi Liu, Rajarshi Saha, Zhen Jia, Youngsuk Park, Jiaji Huang, Shoham Sabach, Yu-Xiang Wang, George Karypis

ICML 2025 RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Quan Wei, Chung-Yiu Yau, Hoi To Wai, Yang Zhao, Dongyeop Kang, Youngsuk Park, Mingyi Hong

AISTATS 2025 Stochastic Rounding for LLM Training: Theory and Practice Kaan Ozkara, Tao Yu, Youngsuk Park

AISTATS 2025 Training LLMs with MXFP4 Albert Tseng, Tao Yu, Youngsuk Park

ICML 2024 Collage: Light-Weight Low-Precision Strategy for LLM Training Tao Yu, Gaurav Gupta, Karthick Gopalswamy, Amith R Mamidala, Hao Zhou, Jeffrey Huynh, Youngsuk Park, Ron Diamant, Anoop Deoras, Luke Huan

NeurIPS 2024 Online Posterior Sampling with a Diffusion Prior Branislav Kveton, Boris N. Oreshkin, Youngsuk Park, Aniket Deshmukh, Rui Song

ICML 2024 Variance-Reduced Zeroth-Order Methods for Fine-Tuning Language Models Tanmay Gautam, Youngsuk Park, Hao Zhou, Parameswaran Raman, Wooseok Ha

ICLRW 2024 Variance-Reduced Zeroth-Order Methods for Fine-Tuning Language Models Tanmay Gautam, Youngsuk Park, Hao Zhou, Parameswaran Raman, Wooseok Ha

AISTATS 2023 But Are You Sure? an Uncertainty-Aware Perspective on Explainable AI Charles Marx, Youngsuk Park, Hilaf Hasson, Yuyang Wang, Stefano Ermon, Luke Huan

ICLR 2023 Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Luke Huan

ICML 2023 Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting Hilaf Hasson, Danielle C. Maddix, Bernie Wang, Gaurav Gupta, Youngsuk Park

AISTATS 2022 Learning Quantile Functions Without Quantile Crossing for Distribution-Free Time Series Forecasting Youngsuk Park, Danielle Maddix, François-Xavier Aubet, Kelvin Kan, Jan Gasthaus, Yuyang Wang

AISTATS 2022 Multivariate Quantile Function Forecaster Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus

AISTATS 2022 Robust Probabilistic Time Series Forecasting Taeho Yoon, Youngsuk Park, Ernest K. Ryu, Yuyang Wang

NeurIPSW 2022 Adaptive Sampling for Probabilistic Forecasting Under Distribution Shift Luca Masserano, Syama Sundar Rangapuram, Shubham Kapoor, Rajbir Singh Nirwan, Youngsuk Park, Michael Bohlke-Schneider

NeurIPSW 2022 But Are You Sure? Quantifying Uncertainty in Model Explanations Charles Thomas Marx, Youngsuk Park, Hilaf Hasson, Bernie Wang, Stefano Ermon, Luke Huan

ICML 2022 Domain Adaptation for Time Series Forecasting via Attention Sharing Xiaoyong Jin, Youngsuk Park, Danielle Maddix, Hao Wang, Yuyang Wang

NeurIPSW 2022 First De-Trend Then Attend: Rethinking Attention for Time-Series Forecasting Xiyuan Zhang, Xiaoyong Jin, Karthick Gopalswamy, Gaurav Gupta, Youngsuk Park, Xingjian Shi, Hao Wang, Danielle C. Maddix, Bernie Wang

NeurIPSW 2022 Towards Reverse Causal Inference on Panel Data: Precise Formulation and Challenges Jiayao Zhang, Youngsuk Park, Danielle C. Maddix, Dan Roth, Bernie Wang

ICML 2021 Variance Reduced Training with Stratified Sampling for Forecasting Models Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean Foster

ICML 2020 Structured Policy Iteration for Linear Quadratic Regulator Youngsuk Park, Ryan Rossi, Zheng Wen, Gang Wu, Handong Zhao

AISTATS 2017 Learning the Network Structure of Heterogeneous Data via Pairwise Exponential Markov Random Fields Youngsuk Park, David Hallac, Stephen P. Boyd, Jure Leskovec