Robust Inverse Covariance Estimation under Noisy Measurements. - Jun-Kun Wang; Shou-de Lin (ICML, 2014)

Parallel Least-Squares Policy Iteration. - Jun-Kun Wang; Shou-De Lin (DSAA, 2016)

Efficient Sampling-Based ADMM for Distributed Data. - Jun-Kun Wang; Shou-De Lin (DSAA, 2016)

On Frank-Wolfe and Equilibrium Computation. - Jacob D. Abernethy; Jun-Kun Wang (NIPS, 2017)

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

Acceleration through Optimistic No-Regret Dynamics. - Jun-Kun Wang; Jacob D. Abernethy (NeurIPS, 2018)

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

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

Optimistic Adaptive Acceleration for Optimization. - Jun-Kun Wang; Xiaoyun Li; Ping Li 001 (CoRR, 2019)

Escaping Saddle Points Faster with Stochastic Momentum. - Jun-Kun Wang; Chi-Heng Lin; Jacob D. Abernethy (ICLR, 2020)

Quickly Finding a Benign Region via Heavy Ball Momentum in Non-Convex Optimization. - Jun-Kun Wang; Jacob D. Abernethy (CoRR, 2020)

Provable Acceleration of Neural Net Training via Polyak's Momentum. - Jun-Kun Wang; Jacob D. Abernethy (CoRR, 2020)

Understanding How Over-Parametrization Leads to Acceleration: A case of learning a single teacher neuron. - Jun-Kun Wang; Jacob D. Abernethy (ACML, 2021)

An Optimistic Acceleration of AMSGrad for Nonconvex Optimization. - Jun-Kun Wang; Xiaoyun Li; Belhal Karimi; Ping Li 001 (ACML, 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 (ICML, 2021)

Understanding Modern Techniques in Optimization: Frank-Wolfe, Nesterov's Momentum, and Polyak's Momentum. - Jun-Kun Wang (CoRR, 2021)

Understanding Modern Techniques in Optimization: Frank-Wolfe, Nesterov's Momentum, and Polyak's Momentum. - Jun-Kun Wang (2021)

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, 2022)

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 (ICLR, 2023)

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

Online Detecting LLM-Generated Texts via Sequential Hypothesis Testing by Betting. - Can Chen; Jun-Kun Wang (CoRR, 2024)

No-regret dynamics in the Fenchel game: a unified framework for algorithmic convex optimization. - Jun-Kun Wang; Jacob D. Abernethy; Kfir Y. Levy (Math. Program., 2024)

Optimistic Interior Point Methods for Sequential Hypothesis Testing by Betting. - Can Chen; Jun-Kun Wang (CoRR, 2025)