137 citations · 296 across the 15 of their papers we have counts for
8 papers · 1 filter
Vision-Language Model Dialog Games for Self-Improvement
Ksenia Konyushkova, Christos Kaplanis, Serkan Cabi +1
The increasing demand for high-quality, diverse training data poses a significant bottleneck in advancing vision-language models (VLMs). This paper presents VLM Dialog Games, a nov…
: Policy Representations with Successor Features
Gianluca Scarpellini, Ksenia Konyushkova, Claudio Fantacci +3
This paper describes , a method for representing behaviors of black box policies as feature vectors. The policy representations capture how the statistics of founda…
Retrieval-Augmented Reinforcement Learning
Anirudh Goyal, Abram L. Friesen, Andrea Banino +13
Most deep reinforcement learning (RL) algorithms distill experience into parametric behavior policies or value functions via gradient updates. While effective, this approach has se…
Active Offline Policy Selection
Ksenia Konyushkova, Yutian Chen, Tom Le Paine +5
This paper addresses the problem of policy selection in domains with abundant logged data, but with a restricted interaction budget. Solving this problem would enable safe evaluati…
Semi-supervised reward learning for offline reinforcement learning
Ksenia Konyushkova, Konrad Zolna, Yusuf Aytar +4
In offline reinforcement learning (RL) agents are trained using a logged dataset. It appears to be the most natural route to attack real-life applications because in domains such a…
Offline Learning from Demonstrations and Unlabeled Experience
Konrad Zolna, Alexander Novikov, Ksenia Konyushkova +6
Behavior cloning (BC) is often practical for robot learning because it allows a policy to be trained offline without rewards, by supervised learning on expert demonstrations. Howev…
Discovering General-Purpose Active Learning Strategies
Ksenia Konyushkova, Raphael Sznitman, Pascal Fua
We propose a general-purpose approach to discovering active learning (AL) strategies from data. These strategies are transferable from one domain to another and can be used in conj…
Learning Active Learning from Data
Ksenia Konyushkova, Raphael Sznitman, Pascal Fua
In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to train a regressor that predicts the expected error reduction for a candidate samp…