Youngsuk Park

According to our database, Youngsuk Park authored at least 30 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Online Posterior Sampling with a Diffusion Prior.

Branislav Kveton, Boris Oreshkin, Youngsuk Park, Aniket Deshmukh, Rui Song

CoRR, 2024

Inference Optimization of Foundation Models on AI Accelerators.

Youngsuk Park, Kailash Budhathoki, Liangfu Chen, Jonas M. Kübler, Jiaji Huang, Matthäus Kleindessner, Jun Huan, Volkan Cevher, Yida Wang, George Karypis

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models.

Tanmay Gautam, Youngsuk Park, Hao Zhou, Parameswaran Raman, Wooseok Ha

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Testing Causality for High Dimensional Data.

Arun Jambulapati, Hilaf Hasson, Youngsuk Park, Yuyang Wang

CoRR, 2023

Adaptive Sampling for Probabilistic Forecasting under Distribution Shift.

Luca Masserano, Syama Sundar Rangapuram, Shubham Kapoor, Rajbir-Singh Nirwan, Youngsuk Park, Michael Bohlke-Schneider

CoRR, 2023

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

Proceedings of the 17th ACM Conference on Recommender Systems, 2023

Training Large-scale Foundation Models on Emerging AI Chips.

Aashiq Muhamed, Christian Bock, Rahul Solanki, Youngsuk Park, Yida Wang, Jun Huan

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting.

Hilaf Hasson, Danielle C. Maddix, Bernie Wang, Gaurav Gupta, Youngsuk Park

Proceedings of the International Conference on Machine Learning, 2023

Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms.

Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Luke Huan

Proceedings of the Eleventh International Conference on Learning Representations, 2023

But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI.

Charles Marx, Youngsuk Park, Hilaf Hasson, Yuyang Wang, Stefano Ermon, Luke Huan

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

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, Yuyang Wang

CoRR, 2022

Towards Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms.

Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Jun Huan

CoRR, 2022

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

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Domain Adaptation for Time Series Forecasting via Attention Sharing.

Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Hao Wang, Yuyang Wang

Proceedings of the International Conference on Machine Learning, 2022

Robust Probabilistic Time Series Forecasting.

Taeho Yoon, Youngsuk Park, Ernest K. Ryu, Yuyang Wang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting.

Youngsuk Park, Danielle C. Maddix, François-Xavier Aubet, Kelvin Kan, Jan Gasthaus, Yuyang Wang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Multivariate Quantile Function Forecaster.

Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Variance Reduction in Training Forecasting Models with Subgroup Sampling.

Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean P. Foster

CoRR, 2021

Attention-based Domain Adaptation for Time Series Forecasting.

Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Yuyang Wang, Xifeng Yan

CoRR, 2021

Variance Reduced Training with Stratified Sampling for Forecasting Models.

Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean P. Foster

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Linear convergence of cyclic SAGA.

Youngsuk Park, Ernest K. Ryu

Optim. Lett., 2020

Structured Policy Iteration for Linear Quadratic Regulator.

Youngsuk Park, Ryan A. Rossi, Zheng Wen, Gang Wu, Handong Zhao

Proceedings of the 37th International Conference on Machine Learning, 2020

Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize.

Youngsuk Park, Sauptik Dhar, Stephen P. Boyd, Mohak Shah

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Optimal Operation of a Plug-in Hybrid Vehicle with Battery Thermal and Degradation Model.

Jongho Kim, Youngsuk Park, John D. Fox, Stephen P. Boyd, William J. Dally

Proceedings of the 2020 American Control Conference, 2020

2019

Linear Quadratic Regulator for Resource-Efficient Cloud Services.

Youngsuk Park, Kanak Mahadik, Ryan A. Rossi, Gang Wu, Handong Zhao

Proceedings of the ACM Symposium on Cloud Computing, SoCC 2019, 2019

2017

Network Inference via the Time-Varying Graphical Lasso.

David Hallac, Youngsuk Park, Stephen P. Boyd, Jure Leskovec

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

Learning the Network Structure of Heterogeneous Data via Pairwise Exponential Markov Random Fields.

Youngsuk Park, David Hallac, Stephen P. Boyd, Jure Leskovec

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2011

A Study on Risk Response against Ship Fire using Robot.

Dea-Woo Park, Youngsuk Park, Jae-Min Nam

J. Inform. and Commun. Convergence Engineering, 2011

Cross-layer resource allocation with multipath routing in wireless multihop and multichannel systems.

Bongjhin Shin, Jinwoo Choe, Byoungik Kang, Daehyoung Hong, Youngsuk Park

J. Commun. Networks, 2011