Ruosong Wang
Assistant Professor
Researches the theoretical foundations of modern machine learning, including reinforcement learning theory.
Ruosong Wang's lab at Peking works on learning theory & optimization and reinforcement learning. 9 papers since 2023, 9 at top venues, mostly ICLR and ICML. Applications close .
At a glance
- papers, 2023–2026
- 2/ year
- at T1 venues
- 100%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 1 papers2026 · T1 1 · collecting
- 4 papers2025 · T1 4
- 1 papers2024 · T1 1
- 3 papers2023 · T1 3
T1 venuesOtherVenues: ICLR, ICML, NeurIPS, AISTATS
Research focuspaper titles that use each topic's words
- Reinforcement learning7 papers
- Learning theory & optimization6 papers
- modern0 papers
- theoretical0 papers
- foundations0 papers
Paper list
From DBLP.
20261 paper
20254 papers
- NeurIPS (T1)Deployment Efficient Reward-Free Exploration with Linear Function Approximation (opens in a new tab)
- ICML (T1)Minimax Optimal Regret Bound for Reinforcement Learning with Trajectory Feedback (opens in a new tab)
- ICLR (T1)Misspecified Q-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error (opens in a new tab)
- ICLR (T1)Regret-Optimal List Replicable Bandit Learning: Matching Upper and Lower Bounds (opens in a new tab)
20241 paper
Where alumni went
Alumni are not listed on the lab homepage.
Apply to PekingFall 2027
Computer Science PhD (international applicants)
- Round
- Main
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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Your notes
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Data
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- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
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