11 citations · 18 across the 18 of their papers we have counts for
5 papers · 1 filter
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning
Yarden As, Bhavya Sukhija, Lenart Treven +3
Reinforcement learning (RL) is ubiquitous in the development of modern AI systems. However, state-of-the-art RL agents require extensive, and potentially unsafe, interactions with…
When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL
Lenart Treven, Bhavya Sukhija, Yarden As +2
Reinforcement learning (RL) excels in optimizing policies for discrete-time Markov decision processes (MDP). However, various systems are inherently continuous in time, making disc…
Safe Exploration Using Bayesian World Models and Log-Barrier Optimization
Yarden As, Bhavya Sukhija, Andreas Krause
A major challenge in deploying reinforcement learning in online tasks is ensuring that safety is maintained throughout the learning process. In this work, we propose CERL, a new me…
Transductive Active Learning: Theory and Applications
Jonas Hübotter, Bhavya Sukhija, Lenart Treven +2
We study a generalization of classical active learning to real-world settings with concrete prediction targets where sampling is restricted to an accessible region of the domain, w…
Active Few-Shot Fine-Tuning
Jonas Hübotter, Bhavya Sukhija, Lenart Treven +2
We study the question: How can we select the right data for fine-tuning to a specific task? We call this data selection problem active fine-tuning and show that it is an instance o…