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🏢 University of Manchester

MetaAligner: Towards Generalizable Multi-Objective Alignment of Language Models
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AI Generated Natural Language Processing Large Language Models 🏢 University of Manchester
MetaAligner: a novel, policy-agnostic, and generalizable method for efficiently aligning LLMs to multiple objectives, even unseen ones, achieving significant and balanced improvements while saving up …
Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series
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Machine Learning Deep Learning 🏢 University of Manchester
GNeuralFlow unveils systemic interactions in irregularly sampled time series by learning a directed acyclic graph representing conditional dependencies, achieving superior performance in classificatio…
Diffusion Twigs with Loop Guidance for Conditional Graph Generation
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AI Generated Machine Learning Deep Learning 🏢 University of Manchester
Twigs: a novel score-based diffusion framework using multiple co-evolving flows and loop guidance for superior conditional graph generation.
Credal Learning Theory
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AI Generated AI Theory Generalization 🏢 University of Manchester
Credal Learning Theory uses convex sets of probabilities to model data distribution variability, providing theoretical risk bounds for machine learning models in dynamic environments.
Aligning Individual and Collective Objectives in Multi-Agent Cooperation
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Machine Learning Reinforcement Learning 🏢 University of Manchester
AI agents learn to cooperate effectively even when individual and group goals clash using the new Altruistic Gradient Adjustment (AgA) algorithm.