Kevin Jamieson
Associate Professor
Studies adaptive experimental design, active learning and multi-armed bandits.
Kevin Jamieson's lab at UW works on reinforcement learning. 29 papers since 2023, 25 at top venues, mostly NeurIPS and AISTATS. 11 alumni. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 86%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 11
Papers by yearfrom the lab homepage, 2023–2026
- 7 papers2026 · T1 7 · collecting
- 6 papers2025 · T1 4
- 11 papers2024 · T1 9
- 5 papers2023 · T1 5
T1 venuesOtherVenues: NeurIPS, AISTATS, ICML, COLT, Findings of ACL
Research focuspaper titles that use each topic's words
- Reinforcement learning13 papers
- bandits4 papers
- active learning3 papers
- experimental design1 paper
- experimental1 paper
Paper list
From the lab homepage.
20267 papers
- AISTATS (T1)Efficient Uncoupled Learning Dynamics with Õ(T^{−1/4}) Last-Iterate Convergence in Bilinear Saddle-Point Problems over Convex Sets under Bandit Feedback
- AISTATS (T1)High Effort, Low Gain: Fundamental Limits of Active Learning for Linear Dynamical Systems
- ICML (T1)Minimax Optimal Strategy for Delayed Observations in Online Reinforcement Learning
- COLT (T1)On The Complexity of Best-Arm Identification in Non-Stationary Linear Bandits
- COLT (T1)On the Power of Adaptivity for ε-Best Arm Identification in Linear Bandits
- AISTATS (T1)Optimal Posterior Sampling for Policy Identification in Tabular Markov Decision Processes
- ICML (T1)Revisiting the Bertrand Paradox via Equilibrium Analysis of No-regret Learners
20256 papers
- NeurIPS (T1)Adapting to Stochastic and Adversarial Losses in Episodic MDPs with Aggregate Bandit Feedback
- COLT (T1)Efficient Near-Optimal Algorithm for Online Shortest Paths in Directed Acyclic Graphs with Bandit Feedback Against Adaptive Adversaries
- ICML (T1)Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals
- Symposium on Algorithmic Game TheoryOn the Limitations and Possibilities of Nash Regret Minimization in Zero-Sum Matrix Games under Noisy Feedback
- NeurIPS (T1)On the Universal Near Optimality of Hedge in Combinatorial Settings
- ACM Transactions on Economics and ComputationQuery-Efficient Algorithms to Find the Unique Nash Equilibrium in a Two-Player Zero-Sum Matrix Game
202411 papers
- AISTATS (T1)A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity
- NeurIPS (T1)Active Learning of Neural Population Dynamics Using Two-Photon Holographic Optogenetics
- NeurIPS (T1)CLIPLoss and Norm-Based Data Selection Methods for Multimodal Contrastive Learning
- Findings of ACLExperimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models
- UAI (T1)Fair Active Learning in Low-Data Regimes
- Journal of DMLRLabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning
- NeurIPS (T1)Minimax Optimal Submodular Optimization with Bandit Feedback
- AISTATS (T1)Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits
- AISTATS (T1)Optimal Exploration is no harder than Thompson Sampling
- NeurIPS (T1)Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL
- NeurIPS (T1)Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning
20235 papers
- NeurIPS (T1)Active representation learning for general task space with applications in robotics
- ICML (T1)Improved Active Multi-Task Representation Learning via Lasso
- AISTATS (T1)Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games
- RSS (T1)Large-Scale Package Manipulation via Learned Metrics of Pick Success
- NeurIPS (T1)Optimal Exploration for Model-Based RL in Nonlinear Systems
Where alumni went11 listed, 0% to industry
Industry 0Academia 2
Also to
- Georgia Tech
- UC Berkeley
Apply to UWFall 2027
UW Allen School Computer Science PhD
- Round
- Single round
- Opens
- GRE
- not required
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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