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Tag: feedback

  • Neural variational inference for cutting feedback during uncertainty propagation

    Neural variational inference for cutting feedback during uncertainty propagation arXiv:2510.10268v1 Announce Type: new Abstract: In many scientific applications, uncertainty of estimates from an earlier (upstream) analysis needs to be propagated in subsequent (downstream) Bayesian analysis, without feedback. Cutting feedback methods, also termed cut-Bayes, achieve this by constructing a cut-posterior distribution that prevents backward information flow.…

  • Pure Exploration with Feedback Graphs

    Pure Exploration with Feedback Graphs arXiv:2503.07824v1 Announce Type: new Abstract: We study the sample complexity of pure exploration in an online learning problem with a feedback graph. This graph dictates the feedback available to the learner, covering scenarios between full-information, pure bandit feedback, and settings with no feedback on the chosen action. While variants of…

  • Combinatorial Reinforcement Learning with Preference Feedback

    Combinatorial Reinforcement Learning with Preference Feedback arXiv:2502.10158v1 Announce Type: new Abstract: In this paper, we consider combinatorial reinforcement learning with preference feedback, where a learning agent sequentially offers an action–an assortment of multiple items to–a user, whose preference feedback follows a multinomial logistic (MNL) model. This framework allows us to model real-world scenarios, particularly those…

  • Low-Rank Contextual Reinforcement Learning from Heterogeneous Human Feedback

    Low-Rank Contextual Reinforcement Learning from Heterogeneous Human Feedback arXiv:2412.19436v1 Announce Type: new Abstract: Reinforcement learning from human feedback (RLHF) has become a cornerstone for aligning large language models with human preferences. However, the heterogeneity of human feedback, driven by diverse individual contexts and preferences, poses significant challenges for reward learning. To address this, we propose…

  • Transform Customer Feedback into Actionable Insights with CrewAI and Streamlit

    Transform Customer Feedback into Actionable Insights with CrewAI and Streamlit Build an AI-powered app to analyze unstructured feedback, generate insightful reports, and create interactive visualizations Continue reading on Towards Data Science ยป Alan Jones Go to original source