Row 8064

Row ID: 8064 | Dataset Entry | Axioma AXP Content Repository

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**Paper**: [https://arxiv.org/abs/2402.10877](https://arxiv.org/abs/2402.10877)

**Abstract**:

>It has long been hypothesised that causal reasoning plays a fundamental role in robust and general intelligence. However, it is not known if agents must learn causal models in order to generalise to new domains, or if other inductive biases are sufficient. We answer this question, showing that any agent capable of satisfying a regret bound under a large set of distributional shifts must have learned an approximate causal model of the data generating process, which converges to the true causal model for optimal agents. We discuss the implications of this result for several research areas including transfer learning and causal inference.

FieldValue
text **Paper**: [https://arxiv.org/abs/2402.10877](https://arxiv.org/abs/2402.10877) **Abstract**: >It has long been hypothesised that causal reasoning plays a fundamental role in robust and general intelligence. However, it is not known if agents must learn causal models in order to generalise to new domains, or if other inductive biases are sufficient. We answer this question, showing that any agent capable of satisfying a regret bound under a large set of distributional shifts must have learned …
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-18
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Raw Record

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  "text": "**Paper**: [https://arxiv.org/abs/2402.10877](https://arxiv.org/abs/2402.10877)\n\n**Abstract**:\n\n>It has long been hypothesised that causal reasoning plays a fundamental role in robust and general intelligence. However, it is not known if agents must learn causal models in order to generalise to new domains, or if other inductive biases are sufficient. We answer this question, showing that any agent capable of satisfying a regret bound under a large set of distributional shifts must have learned an approximate causal model of the data generating process, which converges to the true causal model for optimal agents. We discuss the implications of this result for several research areas including transfer learning and causal inference.",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-18",
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}
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