Summary of The Road Less Scheduled, by Aaron Defazio et al.
The Road Less Scheduled
by Aaron Defazio, Xingyu Alice Yang, Harsh Mehta, Konstantin Mishchenko, Ahmed Khaled, Ashok Cutkosky
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Optimization and Control (math.OC); Machine Learning (stat.ML)
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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 paper proposes a schedule-free approach to learning rate optimization, which achieves state-of-the-art performance across various problems, including convex and large-scale deep learning tasks. Unlike existing schedules that require specification of the optimization stopping step T, this method introduces no additional hyper-parameters and is based on a new theory that unifies scheduling and iterate averaging. The Schedule-Free approach outperforms traditional schedules and momentum-based optimizers, making it a promising alternative for machine learning practitioners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a breakthrough in learning rate optimization by eliminating the need for scheduling. It achieves this by introducing a novel method that combines scheduling and iterate averaging. This approach is not only effective but also simple to implement, with no additional hyper-parameters required. The Schedule-Free AdamW algorithm is the core of the winning entry to the MLCommons 2024 AlgoPerf Algorithmic Efficiency Challenge Self-Tuning track. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Optimization
