Concept Graph & Resume using Claude 3.5 Sonnet | Chat GPT4o | Llama 3:

Resume:

1.- Interpretable machine learning aims to help understand what complex machine learning models are doing to avoid unintended harmful consequences.

2.- Interpretability is important when machine learning is used in high-stakes domains like healthcare where mistakes can be very costly.

3.- The machine learning community has been increasingly working on interpretability tools and techniques over the past decade.

4.- Complex systems and humans not fully understanding them has been an issue before, like with expert systems in the 80s.

5.- The abundance of data and cheap computation today makes machine learning ubiquitous and interpretability more important than ever.

6.- Interpretability is needed when the problem is fundamentally underspecified and you can't cleanly write it in a cost function.

7.- Examples of underspecified problems are self-driving cars, debugging models, and scientific discovery where the right answers aren't fully known.

8.- Interpretability is not needed when expected loss can be reasoned about or the problem is sufficiently well-studied.

9.- Approaches to interpretability include doing it before modeling (data analysis), inherently interpretable models, and post-hoc explanations of black-box models.

10.- Facets is an open-source tool from Google to help visualize and understand datasets before modeling.

11.- Exploratory data analysis, coined by John Tukey, refers to visualizing and investigating properties of the data.

12.- MMD-critic is a method to select prototypical and critical data points to efficiently understand datasets.

13.- Inherently interpretable models include rule-based models, per-feature models like linear/logistic regression, and monotonic models.

14.- Rule-based models like decision trees and rule lists can still get quite complex and difficult for humans to parse.

15.- Generalized additive models learn shape functions for each feature to allow complex but interpretable relationships.

16.- Case-based interpretable models use examples to explain, like prototypes for clusters or criticism to show unrepresented points.

17.- Limitations of case-based models include potentially lacking representative examples and humans overgeneralizing from single cases.

18.- Post-hoc interpretability approaches aim to explain models after they are built, like sensitivity analysis and saliency maps.

19.- Sensitivity analysis involves perturbing inputs and seeing the impact on outputs to understand feature importance and interactions.

20.- LIME explains a classifier's decision on a data point by perturbing it and fitting an interpretable model locally.

21.- Saliency maps take the gradient of the output with respect to the input to show the influence of each feature.

22.- Integrated gradients attribute the prediction of a deep network to its input features using a path integral.

23.- Concept activation vectors show how internal neural representations align with human-interpretable concepts.

24.- Influence functions estimate the impact of each training point on a model's predictions for understanding and debugging.

25.- Monotonic models enforce monotonic relationships between certain features and the output, encoding domain knowledge for better learning with less data.

26.- Example-based explanations work well for complex data points like pieces of code that domain experts can readily understand.

27.- Experts like doctors and data scientists can interactively update prototypes and criticism to align explanations with their knowledge.

28.- Inherently interpretable models may not always be able to represent relationships in a sparse, simulatable way.

29.- Feature sparsity and monotonicity can be useful for interpretability but have limitations in expressive power.

30.- The tutorial raises open questions and discussions around interpretability and calls for more interdisciplinary collaboration, such as with HCI.

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