140 citations · 1.3k across the 125 of their papers we have counts for
88 papers · 1 filter
Online Learning with LLM Experts from Limited Feedback
Wang Wei, Soumyabrata Pal, Koyel Mukherjee +4
We study adaptive routing of prompts to large language model (LLM) experts to maximize response quality in an online setting with limited feedback. We formulate it as a bandit prob…
AdvantageFlow: Regularized Advantage-Weighted RL in Flow Models
Branislav Kveton, Anup Rao, Subhojyoti Mukherjee +2
We present AdvantageFlow, a forward-process reinforcement learning (RL) algorithm for rectified flow models. The algorithm minimizes an advantage-weighted prediction loss, which ma…
Evidence-based anomaly detection in clinical domains
Milos Hauskrecht, Michal Valko, Branislav Kveton +2
Anomaly detection methods can be very useful in identifying interesting or concerning events. In this work, we develop and examine new probabilistic anomaly detection methods that…
Learning from a single labeled face and a stream of unlabeled data
Branislav Kveton, Michal Valko
Face recognition from a single image per person is a challenging problem because the training sample is extremely small. We consider a variation of this problem. In our problem, we…
Online semi-supervised perception: Real-time learning without explicit feedback
Branislav Kveton, Michal Valko, Matthai Phillipose +1
This paper proposes an algorithm for real-time learning without explicit feedback. The algorithm combines the ideas of semi-supervised learning on graphs and online learning. In pa…
Semi-supervised learning with max-margin graph cuts
Branislav Kveton, Michal Valko, Ali Rahimi +1
This paper proposes a novel algorithm for semisupervised learning. This algorithm learns graph cuts that maximize the margin with respect to the labels induced by the harmonic func…