Youngsuk Park
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
25
Venues
7
Active years
2017–2025
Best venue rank
A*
Where they publish
Papers
25 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | AISTATS | Stochastic Rounding for LLM Training: Theory and Practice. | Kaan Ozkara, Tao Yu, Youngsuk Park |
| 2025 | AISTATS | Training LLMs with MXFP4. | Albert Tseng, Tao Yu, Youngsuk Park |
| 2025 | ICML | 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 |
| 2025 | ICML | 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 |
| 2025 | ICML | 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 |
| 2025 | KDD | KDD 2025 Workshop on Inference Optimization for Generative AI. | Panpan Xu, Youngsuk Park, Lin Lee Cheong, Yida Wang, Yiying Zhang, George Karypis, Sherry Marcus |
| 2024 | ICML | Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models. | Tanmay Gautam, Youngsuk Park, Hao Zhou, Parameswaran Raman, Wooseok Ha |
| 2024 | ICML | 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 |
| 2024 | KDD | Inference Optimization of Foundation Models on AI Accelerators. | Youngsuk Park, Kailash Budhathoki, Liangfu Chen, Jonas M. Kbler, Jiaji Huang, Matthus Kleindessner, Jun Huan, Volkan Cevher, Yida Wang, George Karypis |
| 2023 | AISTATS | But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI. | Charles Marx, Youngsuk Park, Hilaf Hasson, Yuyang Wang, Stefano Ermon, Luke Huan |
| 2023 | ICLR | Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. | Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Luke Huan |
| 2023 | ICML | Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting. | Hilaf Hasson, Danielle C. Maddix, Bernie Wang, Gaurav Gupta, Youngsuk Park |
| 2023 | KDD | Training Large-scale Foundation Models on Emerging AI Chips. | Aashiq Muhamed, Christian Bock, Rahul Solanki, Youngsuk Park, Yida Wang, Jun Huan |
| 2023 | RecSys | Trending Now: Modeling Trend Recommendations. | Hao Ding, Branislav Kveton, Yifei Ma, Youngsuk Park, Venkataramana Kini, Yupeng Gu, Ravi Divvela, Fei Wang, Anoop Deoras, Hao Wang |
| 2022 | AISTATS | Multivariate Quantile Function Forecaster. | Kelvin Kan, Franois-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus |
| 2022 | AISTATS | Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting. | Youngsuk Park, Danielle C. Maddix, Franois-Xavier Aubet, Kelvin Kan, Jan Gasthaus, Yuyang Wang |
| 2022 | AISTATS | Robust Probabilistic Time Series Forecasting. | Taeho Yoon, Youngsuk Park, Ernest K. Ryu, Yuyang Wang |
| 2022 | ICML | Domain Adaptation for Time Series Forecasting via Attention Sharing. | Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Hao Wang, Yuyang Wang |
| 2022 | KDD | 8th SIGKDD International Workshop on Mining and Learning from Time Series - Deep Forecasting: Models, Interpretability, and Applications. | Sanjay Purushotham, Jun Huan, Cong Shen, Dongjin Song, Yuyang Wang, Jan Gasthaus, Hilaf Hasson, Youngsuk Park, Sungyong Seo, Yuriy Nevmyvaka |
| 2021 | ICML | Variance Reduced Training with Stratified Sampling for Forecasting Models. | Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean P. Foster |
| 2020 | ICASSP | Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize. | Youngsuk Park, Sauptik Dhar, Stephen P. Boyd, Mohak Shah |
| 2020 | ICML | Structured Policy Iteration for Linear Quadratic Regulator. | Youngsuk Park, Ryan A. Rossi, Zheng Wen, Gang Wu, Handong Zhao |
| 2019 | CLOUD | Linear Quadratic Regulator for Resource-Efficient Cloud Services. | Youngsuk Park, Kanak Mahadik, Ryan A. Rossi, Gang Wu, Handong Zhao |
| 2017 | AISTATS | Learning the Network Structure of Heterogeneous Data via Pairwise Exponential Markov Random Fields. | Youngsuk Park, David Hallac, Stephen P. Boyd, Jure Leskovec |
| 2017 | KDD | Network Inference via the Time-Varying Graphical Lasso. | David Hallac, Youngsuk Park, Stephen P. Boyd, Jure Leskovec |