| 2025 | ICML | Online Detection of LLM-Generated Texts via Sequential Hypothesis Testing by Betting. | Can Chen, Jun-Kun Wang |
| 2023 | ICLR | Continuized Acceleration for Quasar Convex Functions in Non-Convex Optimization. | Jun-Kun Wang, Andre Wibisono |
| 2023 | ICLR | Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation. | Jun-Kun Wang, Andre Wibisono |
| 2023 | ICLR | Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time. | Jun-Kun Wang, Andre Wibisono |
| 2022 | ICML | 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 |
| 2021 | ACML | Understanding How Over-Parametrization Leads to Acceleration: A case of learning a single teacher neuron. | Jun-Kun Wang, Jacob D. Abernethy |
| 2021 | ACML | An Optimistic Acceleration of AMSGrad for Nonconvex Optimization. | Jun-Kun Wang, Xiaoyun Li, Belhal Karimi, Ping Li |
| 2021 | ICML | 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 |
| 2020 | ICLR | Escaping Saddle Points Faster with Stochastic Momentum. | Jun-Kun Wang, Chi-Heng Lin, Jacob D. Abernethy |
| 2019 | AAAI | Revisiting Projection-Free Optimization for Strongly Convex Constraint Sets. | Jarrid Rector-Brooks, Jun-Kun Wang, Barzan Mozafari |
| 2019 | ALT | Online Linear Optimization with Sparsity Constraints. | Jun-Kun Wang, Chi-Jen Lu, Shou-De Lin |
| 2018 | COLT | Faster Rates for Convex-Concave Games. | Jacob D. Abernethy, Kevin A. Lai, Kfir Y. Levy, Jun-Kun Wang |
| 2016 | DSAA | Parallel Least-Squares Policy Iteration. | Jun-Kun Wang, Shou-De Lin |
| 2016 | DSAA | Efficient Sampling-Based ADMM for Distributed Data. | Jun-Kun Wang, Shou-De Lin |
| 2014 | ICML | Robust Inverse Covariance Estimation under Noisy Measurements. | Jun-Kun Wang, Shou-de Lin |