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ORCIDYoungsuk Park
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2020 – today
- 2025
[c23]Albert Tseng, Tao Yu, Youngsuk Park:
Training LLMs with MXFP4. AISTATS 2025: 1630-1638- [c22]Kaan Ozkara, Tao Yu, Youngsuk Park:
Stochastic Rounding for LLM Training: Theory and Practice. AISTATS 2025: 4402-4410 - [i27]Hongyi Liu, Rajarshi Saha, Zhen Jia, Youngsuk Park, Jiaji Huang, Shoham Sabach, Yu-Xiang Wang, George Karypis:
ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs. CoRR abs/2502.00258 (2025) - [i26]Quan Wei, Chung-Yiu Yau, Hoi-To Wai, Yang Zhao, Dongyeop Kang, Youngsuk Park, Mingyi Hong:
RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models. CoRR abs/2502.09003 (2025) - [i25]Kaan Ozkara, Tao Yu, Youngsuk Park:
Stochastic Rounding for LLM Training: Theory and Practice. CoRR abs/2502.20566 (2025) - [i24]Albert Tseng, Tao Yu, Youngsuk Park:
Training LLMs with MXFP4. CoRR abs/2502.20586 (2025) - [i23]Ahmed Khaled, Kaan Ozkara, Tao Yu, Mingyi Hong, Youngsuk Park:
MuonBP: Faster Muon via Block-Periodic Orthogonalization. CoRR abs/2510.16981 (2025) - [i22]Jiin Woo, Shaowei Zhu, Allen Nie, Zhen Jia, Yida Wang, Youngsuk Park:
TritonRL: Training LLMs to Think and Code Triton Without Cheating. CoRR abs/2510.17891 (2025) - [i21]Jaihoon Kim, Rajarshi Saha, Minhyuk Sung, Youngsuk Park:
Demystifying Transition Matching: When and Why It Can Beat Flow Matching. CoRR abs/2510.17991 (2025) - [i20]Song Bian, Tao Yu, Shivaram Venkataraman, Youngsuk Park:
Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs. CoRR abs/2510.18245 (2025) - [i19]Hongyi Liu, Jiaji Huang, Zhen Jia, Youngsuk Park, Yu-Xiang Wang:
Not-a-Bandit: Provably No-Regret Drafter Selection in Speculative Decoding for LLMs. CoRR abs/2510.20064 (2025) - 2024
- [c21]Tanmay Gautam, Youngsuk Park, Hao Zhou, Parameswaran Raman, Wooseok Ha:
Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models. ICML 2024 - [c20]Tao Yu, Gaurav Gupta, Karthick Gopalswamy, Amith R. Mamidala, Hao Zhou, Jeffrey Huynh, Youngsuk Park, Ron Diamant, Anoop Deoras, Luke Huan:
Collage: Light-Weight Low-Precision Strategy for LLM Training. ICML 2024 - [c19]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: 6605-6615 - [c18]Branislav Kveton, Boris Oreshkin, Youngsuk Park, Aniket Deshmukh, Rui Song:
Online Posterior Sampling with a Diffusion Prior. NeurIPS 2024 - [i18]Tanmay Gautam, Youngsuk Park, Hao Zhou, Parameswaran Raman, Wooseok Ha:
Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models. CoRR abs/2404.08080 (2024) - [i17]Tao Yu, Gaurav Gupta, Karthick Gopalswamy, Amith R. Mamidala, Hao Zhou, Jeffrey Huynh, Youngsuk Park, Ron Diamant, Anoop Deoras, Luke Huan:
Collage: Light-Weight Low-Precision Strategy for LLM Training. CoRR abs/2405.03637 (2024) - [i16]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 abs/2407.09111 (2024) - [i15]Branislav Kveton, Boris Oreshkin, Youngsuk Park, Aniket Deshmukh, Rui Song:
Online Posterior Sampling with a Diffusion Prior. CoRR abs/2410.03919 (2024) - [i14]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, Yuyang Wang:
Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization. CoRR abs/2412.05244 (2024) - 2023
- [c17]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: 7375-7391 - [c16]Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Luke Huan:
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. ICLR 2023 - [c15]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: 12616-12632 - [c14]Aashiq Muhamed, Christian Bock, Rahul Solanki, Youngsuk Park, Yida Wang, Jun Huan:
Training Large-scale Foundation Models on Emerging AI Chips. KDD 2023: 5821-5822 - [c13]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: 294-305 - [i13]Luca Masserano, Syama Sundar Rangapuram, Shubham Kapoor, Rajbir-Singh Nirwan, Youngsuk Park, Michael Bohlke-Schneider:
Adaptive Sampling for Probabilistic Forecasting under Distribution Shift. CoRR abs/2302.11870 (2023) - [i12]Arun Jambulapati, Hilaf Hasson, Youngsuk Park, Yuyang Wang:
Testing Causality for High Dimensional Data. CoRR abs/2303.07774 (2023) - [i11]Hilaf Hasson, Danielle C. Maddix, Yuyang Wang, Gaurav Gupta, Youngsuk Park:
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting. CoRR abs/2305.15786 (2023) - 2022
- [c12]Taeho Yoon, Youngsuk Park, Ernest K. Ryu, Yuyang Wang:
Robust Probabilistic Time Series Forecasting. AISTATS 2022: 1336-1358 - [c11]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: 8127-8150 - [c10]Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus:
Multivariate Quantile Function Forecaster. AISTATS 2022: 10603-10621 - [c9]Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Hao Wang, Yuyang Wang:
Domain Adaptation for Time Series Forecasting via Attention Sharing. ICML 2022: 10280-10297 - [c8]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: 4896-4897 - [i10]Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus:
Multivariate Quantile Function Forecaster. CoRR abs/2202.11316 (2022) - [i9]Taeho Yoon, Youngsuk Park, Ernest K. Ryu, Yuyang Wang:
Robust Probabilistic Time Series Forecasting. CoRR abs/2202.11910 (2022) - [i8]Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Jun Huan:
Towards Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. CoRR abs/2207.09572 (2022) - [i7]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 abs/2212.08151 (2022) - 2021
- [c7]Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean P. Foster:
Variance Reduced Training with Stratified Sampling for Forecasting Models. ICML 2021: 7145-7155 - [i6]Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Yuyang Wang, Xifeng Yan:
Attention-based Domain Adaptation for Time Series Forecasting. CoRR abs/2102.06828 (2021) - [i5]Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean P. Foster:
Variance Reduction in Training Forecasting Models with Subgroup Sampling. CoRR abs/2103.02062 (2021) - [i4]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 abs/2111.06581 (2021) - 2020
- [j3]Youngsuk Park, Ernest K. Ryu:
Linear convergence of cyclic SAGA. Optim. Lett. 14(6): 1583-1598 (2020) - [c6]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: 3083-3090 - [c5]Youngsuk Park, Sauptik Dhar, Stephen P. Boyd, Mohak Shah:
Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize. ICASSP 2020: 3597-3601 - [c4]Youngsuk Park, Ryan A. Rossi, Zheng Wen, Gang Wu, Handong Zhao:
Structured Policy Iteration for Linear Quadratic Regulator. ICML 2020: 7521-7531 - [i3]Youngsuk Park, Ryan A. Rossi, Zheng Wen, Gang Wu, Handong Zhao:
Structured Policy Iteration for Linear Quadratic Regulator. CoRR abs/2007.06202 (2020)
2010 – 2019
- 2019
- [c3]Youngsuk Park, Kanak Mahadik, Ryan A. Rossi, Gang Wu, Handong Zhao:
Linear Quadratic Regulator for Resource-Efficient Cloud Services. SoCC 2019: 488-489 - [i2]Youngsuk Park, Sauptik Dhar, Stephen P. Boyd, Mohak Shah:
Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize. CoRR abs/1910.07056 (2019) - 2017
- [c2]Youngsuk Park, David Hallac, Stephen P. Boyd, Jure Leskovec:
Learning the Network Structure of Heterogeneous Data via Pairwise Exponential Markov Random Fields. AISTATS 2017: 1302-1310 - [c1]David Hallac, Youngsuk Park, Stephen P. Boyd, Jure Leskovec:
Network Inference via the Time-Varying Graphical Lasso. KDD 2017: 205-213 - [i1]David Hallac, Youngsuk Park, Stephen P. Boyd, Jure Leskovec:
Network Inference via the Time-Varying Graphical Lasso. CoRR abs/1703.01958 (2017) - 2011
- [j2]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): 221-231 (2011) - [j1]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): 230-234 (2011)
Coauthor Index
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