Summary of A Bayesian Approach to Data Point Selection, by Xinnuo Xu et al.
A Bayesian Approach to Data Point Selection
by Xinnuo Xu, Minyoung Kim, Royson Lee, Brais Martinez, Timothy Hospedales
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents a novel Bayesian approach to data point selection (DPS) in deep learning, which addresses the limitations of existing bi-level optimisation (BLO) formulations. The proposed method views DPS as posterior inference in a Bayesian model, inferring instance-wise weights and neural network parameters jointly using stochastic gradient Langevin MCMC sampling. This approach is more efficient than BLO-based methods and can scale to large language models. The paper demonstrates the effectiveness of the method through controlled experiments in vision and language domains, including automated per-task optimization for instruction fine-tuning datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in machine learning. Right now, it’s easy to get lots of training data that isn’t very good quality, but hard to find high-quality data. The researchers came up with a new way to choose which data points are most important for training a model. They used a special kind of computer simulation to make the process more efficient and accurate. This new method can even handle large amounts of data and help train models that are really good at specific tasks. |
Keywords
» Artificial intelligence » Deep learning » Fine tuning » Inference » Machine learning » Neural network » Optimization
