Jun-Kun Wang

According to our database, Jun-Kun Wang authored at least 21 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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., May, 2024

2023

Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time.

Jun-Kun Wang, Andre Wibisono

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation.

Jun-Kun Wang, Andre Wibisono

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Continuized Acceleration for Quasar Convex Functions in Non-Convex Optimization.

Jun-Kun Wang, Andre Wibisono

Proceedings of the Eleventh International Conference on Learning Representations, 2023

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

Proceedings of the International Conference on Machine Learning, 2022

2021

Understanding Modern Techniques in Optimization: Frank-Wolfe, Nesterov's Momentum, and Polyak's Momentum.

Jun-Kun Wang

CoRR, 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

Proceedings of the 38th International Conference on Machine Learning, 2021

An Optimistic Acceleration of AMSGrad for Nonconvex Optimization.

Jun-Kun Wang, Xiaoyun Li, Belhal Karimi, Ping Li

Proceedings of the Asian Conference on Machine Learning, 2021

Understanding How Over-Parametrization Leads to Acceleration: A case of learning a single teacher neuron.

Jun-Kun Wang, Jacob D. Abernethy

Proceedings of the Asian Conference on Machine Learning, 2021

2020

Provable Acceleration of Neural Net Training via Polyak's Momentum.

Jun-Kun Wang, Jacob D. Abernethy

CoRR, 2020

Quickly Finding a Benign Region via Heavy Ball Momentum in Non-Convex Optimization.

Jun-Kun Wang, Jacob D. Abernethy

CoRR, 2020

Escaping Saddle Points Faster with Stochastic Momentum.

Jun-Kun Wang, Chi-Heng Lin, Jacob D. Abernethy

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Optimistic Adaptive Acceleration for Optimization.

Jun-Kun Wang, Xiaoyun Li, Ping Li

CoRR, 2019

Online Linear Optimization with Sparsity Constraints.

Jun-Kun Wang, Chi-Jen Lu, Shou-De Lin

Proceedings of the Algorithmic Learning Theory, 2019

Revisiting Projection-Free Optimization for Strongly Convex Constraint Sets.

Jarrid Rector-Brooks, Jun-Kun Wang, Barzan Mozafari

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Acceleration through Optimistic No-Regret Dynamics.

Jun-Kun Wang, Jacob D. Abernethy

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Faster Rates for Convex-Concave Games.

Jacob D. Abernethy, Kevin A. Lai, Kfir Y. Levy, Jun-Kun Wang

Proceedings of the Conference On Learning Theory, 2018

2017

On Frank-Wolfe and Equilibrium Computation.

Jacob D. Abernethy, Jun-Kun Wang

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Efficient Sampling-Based ADMM for Distributed Data.

Jun-Kun Wang, Shou-De Lin

Proceedings of the 2016 IEEE International Conference on Data Science and Advanced Analytics, 2016

Parallel Least-Squares Policy Iteration.

Jun-Kun Wang, Shou-De Lin

Proceedings of the 2016 IEEE International Conference on Data Science and Advanced Analytics, 2016

2014

Robust Inverse Covariance Estimation under Noisy Measurements.

Jun-Kun Wang, Shou-de Lin

Proceedings of the 31th International Conference on Machine Learning, 2014