Summary of Towards Stable Machine Learning Model Retraining Via Slowly Varying Sequences, by Dimitris Bertsimas et al.
Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences
by Dimitris Bertsimas, Vassilis Digalakis Jr, Yu Ma, Phevos Paschalidis
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
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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 In this paper, researchers tackle the challenge of retraining machine learning models when new data becomes available. Current approaches focus solely on improving predictive accuracy at each iteration, neglecting the model’s structural integrity and analytical insights across reiterations. The authors propose a framework for finding stable sequences of models that balance predictive power and stability. They develop an optimization formulation ensuring Pareto optimal models with good generalization properties, as well as an efficient algorithm performing well in practice. The framework prioritizes retaining consistent analytical insights crucial for model interpretability, ease of implementation, and user trust. Distance metrics are custom-defined to directly incorporate into the optimization problem. Evaluations span various models (regression, decision trees, boosted trees, and neural networks) across application domains (healthcare, vision, language), including a production pipeline deployment at a major US hospital. The findings suggest that a 2% reduction in predictive power yields a 30% improvement in stability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding the best way to retrain machine learning models when new data comes in. Right now, people focus on making the model better without thinking about how it will change over time. The authors want to solve this problem by finding sequences of models that balance how well they predict things and how stable they are. They come up with a special formula and an efficient way to use it, which makes sure the models don’t get too crazy or unstable. They also make sure the models stay easy to understand and trust for people who use them. The authors test their idea on different types of models and in various fields like healthcare and language processing. |
Keywords
* Artificial intelligence * Generalization * Machine learning * Optimization * Regression
