Search Results for author:
Found 9 papers, 5 papers with code
Transductive Active Learning with Application to Safe Bayesian Optimization
We analyze Safe BO under the lens of a generalization of active learning with concrete prediction targets where sampling is restricted to an accessible region of the domain, while prediction targets may lie outside this region.
When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL
Finally, we propose OTaCoS, an efficient model-based algorithm for our setting.
Safe Exploration Using Bayesian World Models and Log-Barrier Optimization
A major challenge in deploying reinforcement learning in online tasks is ensuring that safety is maintained throughout the learning process.
Active Few-Shot Fine-Tuning
We study the question: How can we select the right data for fine-tuning to a specific task?
Transductive Active Learning: Theory and Applications
We generalize active learning to address real-world settings with concrete prediction targets where sampling is restricted to an accessible region of the domain, while prediction targets may lie outside this region.
Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learning
We introduce PACOH-RL, a novel model-based Meta-Reinforcement Learning (Meta-RL) algorithm designed to efficiently adapt control policies to changing dynamics.
Safe Deep RL for Intraoperative Planning of Pedicle Screw Placement
Spinal fusion surgery requires highly accurate implantation of pedicle screw implants, which must be conducted in critical proximity to vital structures with a limited view of anatomy.
Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning
We introduce a general approach for seeking a stationary point in high dimensional non-linear stochastic optimization problems in which maintaining safety during learning is crucial.
Constrained Policy Optimization via Bayesian World Models
Improving sample-efficiency and safety are crucial challenges when deploying reinforcement learning in high-stakes real world applications.
