3 citations · 7 across the 7 of their papers we have counts for
7 papers
Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuning
Mingtian Zhang, Shawn Lan, Peter Hayes +1
Retrieval Augmented Generation (RAG) has emerged as an effective solution for mitigating hallucinations in Large Language Models (LLMs). The retrieval stage in RAG typically involv…
Active Preference Learning for Large Language Models
William Muldrew, Peter Hayes, Mingtian Zhang +1
As large language models (LLMs) become more capable, fine-tuning techniques for aligning with human intent are increasingly important. A key consideration for aligning these models…
Towards Healing the Blindness of Score Matching
Mingtian Zhang, Oscar Key, Peter Hayes +3
Score-based divergences have been widely used in machine learning and statistics applications. Despite their empirical success, a blindness problem has been observed when using the…
Integrated Weak Learning
Peter Hayes, Mingtian Zhang, Raza Habib +3
We introduce Integrated Weak Learning, a principled framework that integrates weak supervision into the training process of machine learning models. Our approach jointly trains the…
Generalization Gap in Amortized Inference
Mingtian Zhang, Peter Hayes, David Barber
The ability of likelihood-based probabilistic models to generalize to unseen data is central to many machine learning applications such as lossless compression. In this work, we st…
Sample Efficient Model Evaluation
Emine Yilmaz, Peter Hayes, Raza Habib +2
Labelling data is a major practical bottleneck in training and testing classifiers. Given a collection of unlabelled data points, we address how to select which subset to label to…
Spread Divergence
Mingtian Zhang, Peter Hayes, Tom Bird +2
For distributions and with different supports or undefined densities, the divergence may not exist. We define a Sprea…