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Youngsuk Park

18 papers · 2017–2025 · 4 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (18) 🌍 Conference Polyglot (4) πŸƒ Academic Marathon (8) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (9) 🌈 Renaissance Researcher (7) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer ⚑ Prolific Year (5) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (6) πŸ’Ž Century Club (18) ❓ The Questioner

Conferences

ICML (9) AISTATS (7) ICLR (1) NIPS (1)

Top co-authors

Yuyang Wang (5) Tao Yu (3) Hilaf Hasson (3) Luke Huan (3) Bernie Wang (2) Francois-Xavier Aubet (2) Kelvin Kan (2) Jan Gasthaus (2) Danielle C. Maddix (2) Hao Zhou (2)

Keywords

time series forecasting (4) quantile crossing (2) variance reduction (2) uncertainty quantification (2) bayesian inference (2) neural network (2) probabilistic forecasting (2) structure learning (1) posterior sampling (1) adversarial robustness (1) explainable ai (1) laplace approximation (1) quantile regression (1) stratified sampling (1) convergence analysis (1) domain adaptation (1) model-based reinforcement learning (1) attention mechanism (1) source domain (1) policy iteration (1)

Papers

Training LLMs with MXFP4 AISTATS 2025
RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models ICML 2025
Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization ICML 2025
PROXSPARSE: REGULARIZED LEARNING OF SEMI-STRUCTURED SPARSITY MASKS FOR PRETRAINED LLMS ICML 2025
Stochastic Rounding for LLM Training: Theory and Practice AISTATS 2025
Online Posterior Sampling with a Diffusion Prior NIPS 2024
Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models ICML 2024
Collage: Light-Weight Low-Precision Strategy for LLM Training ICML 2024
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms ICLR 2023
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting ICML 2023
But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI AISTATS 2023
Multivariate Quantile Function Forecaster AISTATS 2022
Domain Adaptation for Time Series Forecasting via Attention Sharing ICML 2022
Robust Probabilistic Time Series Forecasting AISTATS 2022
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting AISTATS 2022
Variance Reduced Training with Stratified Sampling for Forecasting Models ICML 2021
Structured Policy Iteration for Linear Quadratic Regulator ICML 2020
Learning the Network Structure of Heterogeneous Data via Pairwise Exponential Markov Random Fields AISTATS 2017