Youngsuk Park

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 36

Top Topics

Linear Quadratic

Regularized Kernel

Defense Mechanisms

Dimensional Data

Top Venues

Publications

Tanmay Gautam, Youngsuk Park, Hao Zhou, Parameswaran Raman, Wooseok Ha
Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models. CoRR (2024)
Youngsuk Park, Kailash Budhathoki, Liangfu Chen, Jonas M. Kübler, Jiaji Huang, Matthäus Kleindessner, Jun Huan, Volkan Cevher, Yida Wang, George Karypis
Inference Optimization of Foundation Models on AI Accelerators. KDD (2024)
Tao Yu, Gaurav Gupta, Karthick Gopalswamy, Amith Mamidala, Hao Zhou, Jeffrey Huynh, Youngsuk Park, Ron Diamant, Anoop Deoras, Luke Huan
Collage: Light-Weight Low-Precision Strategy for LLM Training. CoRR (2024)
Youngsuk Park, Kailash Budhathoki, Liangfu Chen, Jonas M. Kübler, Jiaji Huang, Matthäus Kleindessner, Jun Huan, Volkan Cevher, Yida Wang, George Karypis
Inference Optimization of Foundation Models on AI Accelerators. CoRR (2024)
Hao Ding, Branislav Kveton, Yifei Ma, Youngsuk Park, Venkataramana Kini, Yupeng Gu, Ravi Divvela, Fei Wang, Anoop Deoras, Hao Wang
Trending Now: Modeling Trend Recommendations. RecSys (2023)
Hilaf Hasson, Danielle C. Maddix, Yuyang Wang, Gaurav Gupta, Youngsuk Park
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting. CoRR (2023)
Hilaf Hasson, Danielle C. Maddix, Bernie Wang, Gaurav Gupta, Youngsuk Park
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting. ICML (2023)
Charles Marx, Youngsuk Park, Hilaf Hasson, Yuyang Wang, Stefano Ermon, Luke Huan
But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI. AISTATS (2023)
Luca Masserano, Syama Sundar Rangapuram, Shubham Kapoor, Rajbir-Singh Nirwan, Youngsuk Park, Michael Bohlke-Schneider
Adaptive Sampling for Probabilistic Forecasting under Distribution Shift. CoRR (2023)
Aashiq Muhamed, Christian Bock, Rahul Solanki, Youngsuk Park, Yida Wang, Jun Huan
Training Large-scale Foundation Models on Emerging AI Chips. KDD (2023)
Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Luke Huan
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. ICLR (2023)
Arun Jambulapati, Hilaf Hasson, Youngsuk Park, Yuyang Wang
Testing Causality for High Dimensional Data. CoRR (2023)
Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus
Multivariate Quantile Function Forecaster. CoRR (2022)
Xiyuan Zhang, Xiaoyong Jin, Karthick Gopalswamy, Gaurav Gupta, Youngsuk Park, Xingjian Shi, Hao Wang, Danielle C. Maddix, Yuyang Wang
First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting. CoRR (2022)
Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Jun Huan
Towards Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. CoRR (2022)
Sanjay Purushotham, Jun Huan, Cong Shen, Dongjin Song, Yuyang Wang, Jan Gasthaus, Hilaf Hasson, Youngsuk Park, Sungyong Seo, Yuriy Nevmyvaka
8th SIGKDD International Workshop on Mining and Learning from Time Series - Deep Forecasting: Models, Interpretability, and Applications. KDD (2022)
Taeho Yoon, Youngsuk Park, Ernest K. Ryu, Yuyang Wang
Robust Probabilistic Time Series Forecasting. CoRR (2022)
Youngsuk Park, Danielle C. Maddix, François-Xavier Aubet, Kelvin Kan, Jan Gasthaus, Yuyang Wang
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting. AISTATS (2022)
Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus
Multivariate Quantile Function Forecaster. AISTATS (2022)
Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Hao Wang, Yuyang Wang
Domain Adaptation for Time Series Forecasting via Attention Sharing. ICML (2022)
Taeho Yoon, Youngsuk Park, Ernest K. Ryu, Yuyang Wang
Robust Probabilistic Time Series Forecasting. AISTATS (2022)
Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean Foster
Variance Reduced Training with Stratified Sampling for Forecasting Models. ICML (2021)
Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean Foster
Variance Reduction in Training Forecasting Models with Subgroup Sampling. CoRR (2021)
Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Yuyang Wang, Xifeng Yan
Attention-based Domain Adaptation for Time Series Forecasting. CoRR (2021)
Youngsuk Park, Danielle C. Maddix, François-Xavier Aubet, Kelvin Kan, Jan Gasthaus, Yuyang Wang
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting. CoRR (2021)
Youngsuk Park, Ryan A. Rossi, Zheng Wen, Gang Wu, Handong Zhao
Structured Policy Iteration for Linear Quadratic Regulator. ICML (2020)
Youngsuk Park, Ernest K. Ryu
Linear convergence of cyclic SAGA. Optim. Lett. 14 (6) (2020)
Youngsuk Park, Sauptik Dhar, Stephen P. Boyd, Mohak Shah
Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize. ICASSP (2020)
Jongho Kim, Youngsuk Park, John D. Fox, Stephen P. Boyd, William J. Dally
Optimal Operation of a Plug-in Hybrid Vehicle with Battery Thermal and Degradation Model. ACC (2020)
Youngsuk Park, Ryan A. Rossi, Zheng Wen, Gang Wu, Handong Zhao
Structured Policy Iteration for Linear Quadratic Regulator. CoRR (2020)
Youngsuk Park, Kanak Mahadik, Ryan A. Rossi, Gang Wu, Handong Zhao
Linear Quadratic Regulator for Resource-Efficient Cloud Services. SoCC (2019)
Ryan A. Rossi, Youngsuk Park, Handong Zhao, Kanak Mahadik, Gang Wu
Linear Quadratic Regulator for Resource-Efficient Cloud Services. SoCC (2019)
Youngsuk Park, Sauptik Dhar, Stephen P. Boyd, Mohak Shah
Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize. CoRR (2019)
Youngsuk Park, David Hallac, Stephen P. Boyd, Jure Leskovec
Learning the Network Structure of Heterogeneous Data via Pairwise Exponential Markov Random Fields. AISTATS (2017)
David Hallac, Youngsuk Park, Stephen P. Boyd, Jure Leskovec
Network Inference via the Time-Varying Graphical Lasso. KDD (2017)
David Hallac, Youngsuk Park, Stephen P. Boyd, Jure Leskovec
Network Inference via the Time-Varying Graphical Lasso. CoRR (2017)
Bongjhin Shin, Jinwoo Choe, Byoungik Kang, Daehyoung Hong, Youngsuk Park
Cross-layer resource allocation with multipath routing in wireless multihop and multichannel systems. J. Commun. Networks 13 (3) (2011)
Dea-Woo Park, Youngsuk Park, Jae-Min Nam
A Study on Risk Response against Ship Fire using Robot. J. Inform. and Commun. Convergence Engineering 9 (2) (2011)