Jun-Kun Wang
Assistant Professor
Studies optimization, sampling and online learning theory and their use for robust machine learning under distribution shift.
Jun-Kun Wang's lab at UCSD works on learning theory & optimization, trustworthy & safe ML and data management & big data. 9 papers since 2023, 7 at top venues, mostly ICLR and ICML. 1 PhD student. Applications close .
At a glance
- papers, 2023–2026
- 2/ year
- at T1 venues
- 78%
- PhD students
- 1
- MS students
- 0
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 4 papers2026 · T1 3 · collecting
- 1 papers2025 · T1 1
- 1 papers2024 · T1 0
- 3 papers2023 · T1 3
T1 venuesOtherVenues: ICLR, ICML, TMLR
Research focuspaper titles that use each topic's words
- Learning theory & optimization4 papers
- Trustworthy & safe ML0 papers
- Data management & big data0 papers
- robust machine0 papers
- sampling0 papers
Paper list
From the lab homepage.
20264 papers
- Journal of Optimization Theory and ApplicationsFrictionless Hamiltonian Descent and Coordinate Hamiltonian Descent for Strongly Convex Quadratic Problems (opens in a new tab)
- TMLR (T1)Frictionless Hamiltonian Descent with Discretization and Parallel Optimization (opens in a new tab)
- ICML (T1)Lions and Muons: Optimization via Stochastic Frank-Wolfe (opens in a new tab)
- ICLR (T1)Revisiting Active Sequential Prediction-Powered Mean Estimation (opens in a new tab)
20251 paper
20241 paper
20233 papers
- ICLR (T1)Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time (opens in a new tab)
- ICLR (T1)Continuized Acceleration for Quasar Convex Functions in Non-Convex Optimization (opens in a new tab)
- ICLR (T1)Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UCSDFall 2027
UCSD CSE Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
Similar labsnearest by research summary
- GBOxford
Paul Goldberg
- Learning theory & optimization
- SGNTU
Yeshwanth Cherapanamjeri
- Learning theory & optimization
- USCMU
Matt Fredrikson
- Trustworthy & safe ML
- CHEPFL
Caglar Gulcehre
- Reinforcement learning
- USUW
Lillian Ratliff
- Learning theory & optimization
- USUW
Maryam Fazel
- Learning theory & optimization
Your notes
Save this lab to keep notes.
Data
Some numbers could not be verified:
- The alumni list could not be found on the lab site.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Not collected
Tiers are venue classifications, not lab rankings.
UCSDcloses