Jun-Kun Wang

 

I am an assistant professor in the Department of Electrical and Computer Engineering and the Halicio─člu Data Science Institute at UC San Diego. My research interests are optimization and machine learning. I like discovering connections between different research areas, e.g., optimization and no-regret learning, optimization and sampling, optimization and tackling distribution shifts.

I was a postdoc at Yale University working with Professor Andre Wibisono. I received my CS PhD from Georgia Tech and was very fortunate to be advised by Professor Jacob Abernethy. I hold my M.S. in Communication Engineering and B.S. in Electrical Engineering from National Taiwan University.

Email: jkw005 [at] ucsd [dot] edu

I am looking for strongly motivated students interested in the theoretical foundations and algorithmic foundations of optimization and machine learning. I am also seeking students with strong coding skills who are eager to make optimization and machine learning more efficient and more reliable. Feel free to drop me an email with your CV and transcripts.

Teaching:

Winter 2024 DSC 211 Introduction to Optimization

Spring 2024 ECE 273 Convex Optimization and Applications

Preprints:

Hamiltonian Descent and Coordinate Hamiltonian Descent
Jun-Kun Wang. arXiv:2402.13988. 2024

Publications: *Corresponding Author/*Presenting Author

No-Regret Dynamics in the Fenchel Game: A Unified Framework for Algorithmic Convex Optimization.
Jun-Kun Wang, Jacob Abernethy, Kfir Y. Levy.
Mathematical Programming 2023

Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time
Jun-Kun Wang and Andre Wibisono
In ICLR (International Conference on Learning Representations), 2023.

Continuized Acceleration for Quasar Convex Functions in Non-Convex Optimization
Jun-Kun Wang and Andre Wibisono
In ICLR (International Conference on Learning Representations), 2023.

Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation
Jun-Kun Wang and Andre Wibisono
In ICLR (International Conference on Learning Representations), 2023.

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, and Bin Hu
In ICML (International Conference on Machine Learning), 2022.

Understanding Modern Techniques in Optimization: Frank-Wolfe, Nesterov's Momentum, and Polyak's Momentum.
PhD Dissertation at Georgia Tech. 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, and Jacob Abernethy.
In ICML (International Conference on Machine Learning), 2021.

Understanding How Over-Parametrization Leads to Acceleration: A case of learning a single teacher neuron
Jun-Kun Wang and Jacob Abernethy.
In ACML (Asian Conference on Machine Learning), 2021.

Escape Saddle Points Faster with Stochastic Momentum.
Jun-Kun Wang, Chi-Heng Lin, and Jacob Abernethy.
In ICLR (International Conference on Learning Representations), 2020.

Online Linear Optimization with Sparsity Constraints
*Jun-Kun Wang, * Chi-Jen Lu, and Shou-De Lin.
In ALT (International Conference on Algorithmic Learning Theory), 2019.

Revisiting Projection-Free Optimization For Strongly Convex Constraint Sets
Jarrid Rector-Brooks, Jun-Kun Wang, and Barzan Mozafari.
In AAAI 33, 2019.

Acceleration through Optimistic No-Regret Dynamics
*Jun-Kun Wang and Jacob Abernethy.
In NeurIPS (Annual Conference on Neural Information Processing Systems), 2018. (Spotlight) paper

Faster Rates for Convex-Concave Games
(name order) Jacob Abernethy, Kevin Lai, Kfir Levy, and *Jun-Kun Wang.
In COLT (Computational Learning Theory), 2018.

On Frank-Wolfe and Equilibrium Computation
Jacob Abernethy and *Jun-Kun Wang.
In NeurIPS (Annual Conference on Neural Information Processing Systems), 2017. (Spotlight) paper supplementary

Efficient Sampling-based ADMM for Distributed Data
*Jun-Kun Wang, Shou-De Lin.
In DSAA (IEEE International Conference on Data Science and Advanced Analytics), 2016. code

Parallel Least-Squares Policy Iteration
*Jun-Kun Wang, Shou-De Lin.
In DSAA (IEEE International Conference on Data Science and Advanced Analytics), 2016.

Robust Inverse Covariance Estimation under Noisy Measurements
*Jun-Kun Wang, Shou-De Lin.
In ICML (International Conference on Machine Learning), 2014. paper slide