Noah Golowich
Assistant Professor
Studies learning theory, including foundations of LLMs and generative models, multi-agent learning and reinforcement learning theory.
Noah Golowich's lab at UT Austin works on learning theory & optimization, reinforcement learning and planning, control & multi-robot systems. 25 papers since 2023, 23 at top venues, mostly COLT and STOC. Applications close .
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 92%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 5 papers2026 · T1 5 · collecting
- 3 papers2025 · T1 3
- 10 papers2024 · T1 8
- 7 papers2023 · T1 7
T1 venuesOtherVenues: COLT, STOC, NeurIPS, ICML, ICLR
Research focuspaper titles that use each topic's words
- Reinforcement learning12 papers
- Learning theory & optimization11 papers
- Planning, control & multi-robot systems4 papers
- learning theory0 papers
- multi-agent learning0 papers
Paper list
From the lab homepage.
20265 papers
- STOC (T1)Improved Pseudorandom Codes from Permuted Puzzles (opens in a new tab)
- COLT (T1)Learning with Simulators: No Regret in a Computationally Bounded World (opens in a new tab)
- ICLR (T1)Sequences of Logits Reveal the Low Rank Structure of Language Models (opens in a new tab)
- ICML (T1)Subliminal Effects in Your Data: A General Mechanism via Log-Linearity (opens in a new tab)
- ICLR (T1)The Coverage Principle: How Pre-training Enables Post-Training (opens in a new tab)
20253 papers
202410 papers
- NeurIPS (T1)Edit Distance Robust Watermarks for Language Models (opens in a new tab)
- FOCS (T1)Exploration is Harder than Prediction: Cryptographically Separating Reinforcement Learning from Supervised Learning (opens in a new tab)
- STOC (T1)Exploring and Learning in Sparse Linear MDPs without Computationally Intractable Oracles (opens in a new tab)
- STOC (T1)From External to Swap Regret 2.0: An Efficient Reduction and Oblivious Adversary for Large Action Spaces (opens in a new tab)
- COLT (T1)Is Efficient PAC Learning Possible with an Oracle That Responds 'Yes' or 'No'? (opens in a new tab)
- COLT (T1)Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions (opens in a new tab)
- COLT (T1)Near-Optimal Learning and Planning in Separated Latent MDPs (opens in a new tab)
- NeurIPS (T1)Online Control in Population Dynamics (opens in a new tab)
- ITCSSmooth Nash Equilibria: Algorithms and Complexity (opens in a new tab)
- Reinforcement Learning ConferenceThe Role of Inherent Bellman Error in Offline Reinforcement Learning with Linear Function Approximation (opens in a new tab)
20237 papers
- ICML (T1)Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games (opens in a new tab)
- NeurIPS (T1)Model-free reinforcement learning with the decision-estimation coefficient (opens in a new tab)
- COLT (T1)On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring (opens in a new tab)
- STOC (T1)Planning and Learning in Partially Observable Systems via Filter Stability (opens in a new tab)
- COLT (T1)STay-ON-the-Ridge: Guaranteed Convergence to Local Minimax Equilibrium in Nonconvex-Nonconcave Games (opens in a new tab)
- COLT (T1)The complexity of Markov equilibrium in stochastic games (opens in a new tab)
- COLT (T1)Tight guarantees for interactive decision making with the decision-estimation coefficient (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UT AustinFall 2027
Computer Science PhD
- Round
- Single round (Dec 15)
- Opens
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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Data
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