Answers.Body: <p>I found <a href="https://kilthub.cmu.edu/articles/thesis/Towards_Robust_and_Resilient_Machine_Learning/19552420" rel="nofollow noreferrer">this guy's thesis</a> to be very helpful in understanding it:</p>
  <ul>
  <li><strong>Robustness: The model can be trained even with noisy and corrupted data.</strong> Simply put, if we add small perturbations to the image, can it still recognize it correctly? This one is important in Adversarial attack, where an attacker <em>purposely</em> adds the pertubation to misguide the models.</li>
  <li><strong>Reliability: After training and when deployed in the real-world, the model should not break down under benign shifts of the distribution</strong>. This is straightforward to understand: how good the model is on a set that has different distribution than the training set, e.g., I train on photos but evaluate on paintings.</li>
  <li><strong>Resilience: The modeling procedure should work under model mis-specification</strong>, i.e. even when the modeling assumption breaks down, the model should find the best possible solution. For example, when the hardware breaks, can the model still perform as good?</li>
  </ul>
  
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Answers.CreationDate: 2023-03-05T05:09:46.803Z
Answers.LastActivityDate: 2023-03-05T05:09:46.803Z
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Body: <p>In the context of the Machine Learning model, is there any clear definition of reliability, resiliency, and robustness of a model? I saw some papers discuss different things (e.g. attacked model, fault model, noisy data, etc.) when they talk about these terms.</p>
  
Tags: <machine-learning><definitions>
Title: What's the difference between Reliability, Resiliency, and Robustness?
UserDisplayName: malioboro
ThreadText: # What's the difference between Reliability, Resiliency, and Robustness?
  
  (Asked by: username_0 on 2023-03-05)
  
  In the context of the Machine Learning model, is there any clear definition of reliability, resiliency, and robustness of a model? I saw some papers discuss different things (e.g. attacked model, fault model, noisy data, etc.) when they talk about these terms.
  
  **Tags:** machine-learning, definitions
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2023-03-05. Score: 1 | Accepted answer
  
  I found [this guy's thesis](https://kilthub.cmu.edu/articles/thesis/Towards_Robust_and_Resilient_Machine_Learning/19552420) to be very helpful in understanding it:
  
  * **Robustness: The model can be trained even with noisy and corrupted data.** Simply put, if we add small perturbations to the image, can it still recognize it correctly? This one is important in Adversarial attack, where an attacker *purposely* adds the pertubation to misguide the models.
  * **Reliability: After training and when deployed in the real-world, the model should not break down under benign shifts of the distribution**. This is straightforward to understand: how good the model is on a set that has different distribution than the training set, e.g., I train on photos but evaluate on paintings.
  * **Resilience: The modeling procedure should work under model mis-specification**, i.e. even when the modeling assumption breaks down, the model should find the best possible solution. For example, when the hardware breaks, can the model still perform as good?
  
  
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