Summary of Gradual Domain Adaptation Via Manifold-constrained Distributionally Robust Optimization, by Amir Hossein Saberi et al.
Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimization
by Amir Hossein Saberi, Amir Najafi, Ala Emrani, Amin Behjati, Yasaman Zolfimoselo, Mahdi Shadrooy, Abolfazl Motahari, Babak H. Khalaj
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 The paper proposes a methodology for gradual domain adaptation in manifold-constrained data distributions using Distributionally Robust Optimization (DRO) with an adaptive Wasserstein radius. The goal is to develop a classification model that can be adapted across multiple datasets, each with a gradual shift from the previous one, while ensuring the error remains bounded. The method relies on a newly introduced compatibility measure, which characterizes the error propagation dynamics along the sequence. Theoretical results show that for inadequately constrained distributions, the error can escalate exponentially, whereas for appropriately constrained distributions, the error can be linear or eradicated. Experimental results support these findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making a computer learn to classify things as they change slowly over time. It’s like trying to teach a kid to recognize different animals in a zoo, but the animals are moving and changing gradually. The researchers want to find a way to make sure the kid (computer) doesn’t get confused and starts making mistakes. They propose a new method that uses something called Distributionally Robust Optimization (DRO) to keep the errors low. They also introduce a new measure they call “compatibility” which helps them understand how well their method works. |
Keywords
» Artificial intelligence » Classification » Domain adaptation » Optimization
