Wang, Jun-Kun

16 publications

ICML 2025 Online Detection of LLM-Generated Texts via Sequential Hypothesis Testing by Betting Can Chen, Jun-Kun Wang

ICLR 2023 Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time Jun-Kun Wang, Andre Wibisono

ICLR 2023 Continuized Acceleration for Quasar Convex Functions in Non-Convex Optimization Jun-Kun Wang, Andre Wibisono

ICLR 2023 Towards Understanding GD with Hard and Conjugate Pseudo-Labels for Test-Time Adaptation Jun-Kun Wang, Andre Wibisono

ICML 2022 Provable Acceleration of Heavy Ball Beyond Quadratics for a Class of Polyak-Lojasiewicz Functions When the Non-Convexity Is Averaged-Out Jun-Kun Wang, Chi-Heng Lin, Andre Wibisono, Bin Hu

ICML 2021 A Modular Analysis of Provable Acceleration via Polyak’s Momentum: Training a Wide ReLU Network and a Deep Linear Network Jun-Kun Wang, Chi-Heng Lin, Jacob D Abernethy

ACML 2021 An Optimistic Acceleration of AMSGrad for Nonconvex Optimization Jun-Kun Wang, Xiaoyun Li, Belhal Karimi, Ping Li

ACML 2021 Understanding How Over-Parametrization Leads to Acceleration: A Case of Learning a Single Teacher Neuron Jun-Kun Wang, Jacob Abernethy

ICLR 2020 Escaping Saddle Points Faster with Stochastic Momentum Jun-Kun Wang, Chi-Heng Lin, Jacob Abernethy

ICLR 2020 Optimistic Adaptive Acceleration for Optimization Jun-Kun Wang, Xiaoyun Li, Ping Li

ALT 2019 Online Linear Optimization with Sparsity Constraints Jun-Kun Wang, Chi-Jen Lu, Shou-De Lin

AAAI 2019 Revisiting Projection-Free Optimization for Strongly Convex Constraint Sets Jarrid Rector-Brooks, Jun-Kun Wang, Barzan Mozafari

NeurIPS 2018 Acceleration Through Optimistic No-Regret Dynamics Jun-Kun Wang, Jacob D. Abernethy

COLT 2018 Faster Rates for Convex-Concave Games Jacob D. Abernethy, Kevin A. Lai, Kfir Y. Levy, Jun-Kun Wang

NeurIPS 2017 On Frank-Wolfe and Equilibrium Computation Jacob D. Abernethy, Jun-Kun Wang

ICML 2014 Robust Inverse Covariance Estimation Under Noisy Measurements Jun-Kun Wang, Shou-de Lin