Youngsuk Park - RankMe

57 papers A* 15A 8C 1Misc 1Journal 30Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Demystifying Transition Matching: When and Why It Can Beat Flow Matching. CoRR Jaihoon Kim, Rajarshi Saha, Minhyuk Sung, Youngsuk Park
2025	A*	`conf`	Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization. ICML 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	—	`conf`	KDD 2025 Workshop on Inference Optimization for Generative AI. KDD (2) Panpan Xu, Youngsuk Park, Lin Lee Cheong, Yida Wang, Yiying Zhang, George Karypis, Sherry Marcus
2025	J	`jnl`	MuonBP: Faster Muon via Block-Periodic Orthogonalization. CoRR Ahmed Khaled, Kaan Ozkara, Tao Yu, Mingyi Hong, Youngsuk Park
2025	J	`jnl`	Not-a-Bandit: Provably No-Regret Drafter Selection in Speculative Decoding for LLMs. CoRR Hongyi Liu, Jiaji Huang, Zhen Jia, Youngsuk Park, Yu-Xiang Wang
2025	J	`jnl`	ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs. CoRR Hongyi Liu, Rajarshi Saha, Zhen Jia, Youngsuk Park, Jiaji Huang, Shoham Sabach, Yu-Xiang Wang, George Karypis
2025	A*	`conf`	Proxsparse: Regularized Learning of Semi-Structured Sparsity masks for Pretrained LLMS. ICML Hongyi Liu, Rajarshi Saha, Zhen Jia, Youngsuk Park, Jiaji Huang, Shoham Sabach, Yu-Xiang Wang, George Karypis
2025	A*	`conf`	RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models. ICML Quan Wei, Chung-Yiu Yau, Hoi-To Wai, Yang Zhao, Dongyeop Kang, Youngsuk Park, Mingyi Hong
2025	J	`jnl`	RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models. CoRR Quan Wei, Chung-Yiu Yau, Hoi-To Wai, Yang Zhao, Dongyeop Kang, Youngsuk Park, Mingyi Hong
2025	J	`jnl`	Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs. CoRR Song Bian, Tao Yu, Shivaram Venkataraman, Youngsuk Park
2025	A	`conf`	Stochastic Rounding for LLM Training: Theory and Practice. AISTATS Kaan Ozkara, Tao Yu, Youngsuk Park
2025	J	`jnl`	Stochastic Rounding for LLM Training: Theory and Practice. CoRR Kaan Ozkara, Tao Yu, Youngsuk Park
2025	A	`conf`	Training LLMs with MXFP4. AISTATS Albert Tseng, Tao Yu, Youngsuk Park
2025	J	`jnl`	Training LLMs with MXFP4. CoRR Albert Tseng, Tao Yu, Youngsuk Park
2025	J	`jnl`	TritonRL: Training LLMs to Think and Code Triton Without Cheating. CoRR Jiin Woo, Shaowei Zhu, Allen Nie, Zhen Jia, Yida Wang, Youngsuk Park
2024	A*	`conf`	Collage: Light-Weight Low-Precision Strategy for LLM Training. ICML Tao Yu, Gaurav Gupta, Karthick Gopalswamy, Amith R. Mamidala, Hao Zhou, Jeffrey Huynh, Youngsuk Park, Ron Diamant, Anoop Deoras, Luke Huan
2024	J	`jnl`	Collage: Light-Weight Low-Precision Strategy for LLM Training. CoRR Tao Yu, Gaurav Gupta, Karthick Gopalswamy, Amith R. Mamidala, Hao Zhou, Jeffrey Huynh, Youngsuk Park, Ron Diamant, Anoop Deoras, Luke Huan
2024	J	`jnl`	Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization. CoRR 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
2024	A*	`conf`	Inference Optimization of Foundation Models on AI Accelerators. KDD Youngsuk Park, Kailash Budhathoki, Liangfu Chen, Jonas M. Kübler, Jiaji Huang, Matthäus Kleindessner, Jun Huan, Volkan Cevher, Yida Wang, George Karypis
2024	J	`jnl`	Inference Optimization of Foundation Models on AI Accelerators. CoRR Youngsuk Park, Kailash Budhathoki, Liangfu Chen, Jonas M. Kübler, Jiaji Huang, Matthäus Kleindessner, Jun Huan, Volkan Cevher, Yida Wang, George Karypis
2024	A*	`conf`	Online Posterior Sampling with a Diffusion Prior. NeurIPS Branislav Kveton, Boris Oreshkin, Youngsuk Park, Aniket Deshmukh, Rui Song
2024	J	`jnl`	Online Posterior Sampling with a Diffusion Prior. CoRR Branislav Kveton, Boris Oreshkin, Youngsuk Park, Aniket Deshmukh, Rui Song
2024	A*	`conf`	Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models. ICML Tanmay Gautam, Youngsuk Park, Hao Zhou, Parameswaran Raman, Wooseok Ha
2024	J	`jnl`	Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models. CoRR Tanmay Gautam, Youngsuk Park, Hao Zhou, Parameswaran Raman, Wooseok Ha
2023	J	`jnl`	Adaptive Sampling for Probabilistic Forecasting under Distribution Shift. CoRR Luca Masserano, Syama Sundar Rangapuram, Shubham Kapoor, Rajbir-Singh Nirwan, Youngsuk Park, Michael Bohlke-Schneider
2023	A	`conf`	But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI. AISTATS Charles Marx, Youngsuk Park, Hilaf Hasson, Yuyang Wang, Stefano Ermon, Luke Huan
2023	A*	`conf`	Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. ICLR Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Luke Huan
2023	J	`jnl`	Testing Causality for High Dimensional Data. CoRR Arun Jambulapati, Hilaf Hasson, Youngsuk Park, Yuyang Wang
2023	A*	`conf`	Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting. ICML Hilaf Hasson, Danielle C. Maddix, Bernie Wang, Gaurav Gupta, Youngsuk Park
2023	J	`jnl`	Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting. CoRR Hilaf Hasson, Danielle C. Maddix, Yuyang Wang, Gaurav Gupta, Youngsuk Park
2023	A*	`conf`	Training Large-scale Foundation Models on Emerging AI Chips. KDD Aashiq Muhamed, Christian Bock, Rahul Solanki, Youngsuk Park, Yida Wang, Jun Huan
2023	A	`conf`	Trending Now: Modeling Trend Recommendations. RecSys Hao Ding, Branislav Kveton, Yifei Ma, Youngsuk Park, Venkataramana Kini, Yupeng Gu, Ravi Divvela, Fei Wang, Anoop Deoras, Hao Wang
2022	A*	`conf`	8th SIGKDD International Workshop on Mining and Learning from Time Series - Deep Forecasting: Models, Interpretability, and Applications. KDD Sanjay Purushotham, Jun Huan, Cong Shen, Dongjin Song, Yuyang Wang, Jan Gasthaus, Hilaf Hasson, Youngsuk Park, Sungyong Seo, Yuriy Nevmyvaka
2022	A*	`conf`	Domain Adaptation for Time Series Forecasting via Attention Sharing. ICML Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Hao Wang, Yuyang Wang
2022	J	`jnl`	First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting. CoRR Xiyuan Zhang, Xiaoyong Jin, Karthick Gopalswamy, Gaurav Gupta, Youngsuk Park, Xingjian Shi, Hao Wang, Danielle C. Maddix, Yuyang Wang
2022	A	`conf`	Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting. AISTATS Youngsuk Park, Danielle C. Maddix, François-Xavier Aubet, Kelvin Kan, Jan Gasthaus, Yuyang Wang
2022	A	`conf`	Multivariate Quantile Function Forecaster. AISTATS Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus
2022	J	`jnl`	Multivariate Quantile Function Forecaster. CoRR Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus
2022	A	`conf`	Robust Probabilistic Time Series Forecasting. AISTATS Taeho Yoon, Youngsuk Park, Ernest K. Ryu, Yuyang Wang
2022	J	`jnl`	Robust Probabilistic Time Series Forecasting. CoRR Taeho Yoon, Youngsuk Park, Ernest K. Ryu, Yuyang Wang
2022	J	`jnl`	Towards Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. CoRR Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Jun Huan
2021	J	`jnl`	Attention-based Domain Adaptation for Time Series Forecasting. CoRR Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Yuyang Wang, Xifeng Yan
2021	J	`jnl`	Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting. CoRR Youngsuk Park, Danielle C. Maddix, François-Xavier Aubet, Kelvin Kan, Jan Gasthaus, Yuyang Wang
2021	A*	`conf`	Variance Reduced Training with Stratified Sampling for Forecasting Models. ICML Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean P. Foster
2021	J	`jnl`	Variance Reduction in Training Forecasting Models with Subgroup Sampling. CoRR Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean P. Foster
2020	J	`jnl`	Linear convergence of cyclic SAGA. Optim. Lett. Youngsuk Park, Ernest K. Ryu
2020	C	`conf`	Optimal Operation of a Plug-in Hybrid Vehicle with Battery Thermal and Degradation Model. ACC Jongho Kim, Youngsuk Park, John D. Fox, Stephen P. Boyd, William J. Dally
2020	A*	`conf`	Structured Policy Iteration for Linear Quadratic Regulator. ICML Youngsuk Park, Ryan A. Rossi, Zheng Wen, Gang Wu, Handong Zhao
2020	J	`jnl`	Structured Policy Iteration for Linear Quadratic Regulator. CoRR Youngsuk Park, Ryan A. Rossi, Zheng Wen, Gang Wu, Handong Zhao
2020	Misc	`conf`	Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize. ICASSP Youngsuk Park, Sauptik Dhar, Stephen P. Boyd, Mohak Shah
2019	—	`conf`	Linear Quadratic Regulator for Resource-Efficient Cloud Services. SoCC Youngsuk Park, Kanak Mahadik, Ryan A. Rossi, Gang Wu, Handong Zhao
2019	J	`jnl`	Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize. CoRR Youngsuk Park, Sauptik Dhar, Stephen P. Boyd, Mohak Shah
2017	A	`conf`	Learning the Network Structure of Heterogeneous Data via Pairwise Exponential Markov Random Fields. AISTATS Youngsuk Park, David Hallac, Stephen P. Boyd, Jure Leskovec
2017	A*	`conf`	Network Inference via the Time-Varying Graphical Lasso. KDD David Hallac, Youngsuk Park, Stephen P. Boyd, Jure Leskovec
2017	J	`jnl`	Network Inference via the Time-Varying Graphical Lasso. CoRR David Hallac, Youngsuk Park, Stephen P. Boyd, Jure Leskovec
2011	J	`jnl`	A Study on Risk Response against Ship Fire using Robot. J. Inform. and Commun. Convergence Engineering Dea-Woo Park, Youngsuk Park, Jae-Min Nam
2011	J	`jnl`	Cross-layer resource allocation with multipath routing in wireless multihop and multichannel systems. J. Commun. Networks Bongjhin Shin, Jinwoo Choe, Byoungik Kang, Daehyoung Hong, Youngsuk Park