Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Gemini Adv | Llama 3:

Resume:

1.-Machine learning is useful when formal problem specifications are lacking. With enough data, learning systems can outperform heuristic programs.

2.-Statistical algorithms optimize for the training data, but may miss the point and not generalize well due to spurious correlations.

3.-Nature doesn't shuffle data like we do in machine learning. Data comes from different environments with different biases.

4.-Robust learning aims to minimize the maximum error across environments. This interpolates but does not extrapolate beyond convex combinations of environments.

5.-In some applications, extrapolation to new environments is needed, not just interpolation between training environments. Search engines are one example.

6.-Invariance is related to causation. To predict interventions, you need the intervention properties and what remains invariant.

7.-The goal is to learn a representation in which an invariant predictor exists across environments, ignoring spurious correlations.

8.-Peters et al. 2016 considered interventions on known variables in a causal graph. The invariant predictor recovers the target's direct causes.

9.-Adversarial domain adaptation learns an environment-independent representation, but the fairness and invariance perspectives have key differences regarding dependence on the target.

10.-The robust approach defines an a priori family of environments. Using multiple environments to define the domain enables extrapolation via invariance.

11.-For linear regression, the matrix S is sought such that a vector v simultaneously minimizes error in all environments. Solutions exist when gradients are linearly dependent.

12.-High-rank invariant solutions can be found by solving along the cosine direction between weight vector w and the space spanned by cost gradients.

13.-Inserting a frozen dummy layer and penalizing its gradient achieves invariance without linear assumptions. This extends to neural networks.

14.-A toy "Colored MNIST" example shows how relying on unstable features like color can be overcome by penalizing cross-environment variance.

15.-The invariance regularizer is highly non-convex. Tractability and scaling remain challenging. Realizable problems (where a perfect invariant predictor exists) differ from unrealizable ones.

16.-In realizable supervised learning, asymptotic invariance holds over the union of supports of the training environments. Large datasets are needed.

17.-In non-realizable settings, the challenge is finding an invariant representation and predictor to enable extrapolation. In realizable settings, it's about data efficiency.

18.-Machine learning uses a statistical proxy and doesn't shuffle data like nature does. Utilizing environment information could improve stability.

19.-Invariance across environments provides extrapolation, not just interpolation. This challenges the notion that extrapolation fails in high dimensions.

20.-Invariance is related to causation. Stable properties inform causal inference when combined with knowledge of interventions.

21.-Where invariance doesn't naturally hold, learning an invariant representation can enforce it, with interesting mathematical properties.

22.-Realizable supervised problems, where a perfect invariant predictor exists, pose different challenges around efficiently finding the predictor, rather than its existence.

23.-Meta-learning aims to learn transferable representations, while invariance focuses on mathematically characterizing stable properties to enable extrapolation and causal inference.

24.-With enough data and compute, large models may exhibit invariance, but an explicit invariance approach provides clearer understanding and guarantees.

25.-The key ideas are: learn stable properties across environments to enable extrapolation, relate invariance to causation, and tailor methods to realizable vs non-realizable regimes.

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