The paper introduces a model for automatically learning inductive bias, which is crucial in machine learning for selecting a hypothesis space that is large enough to contain a solution to the problem but small enough to ensure reliable generalization from limited training data. The model assumes the learner is embedded in an environment of related learning tasks, allowing it to sample from multiple tasks and search for a hypothesis space that performs well on a sufficient number of training tasks. The central assumption is that the learner can improve its performance on novel tasks by learning multiple related tasks within the environment. The paper derives explicit bounds on the sample complexity required for good generalization, showing that learning multiple tasks can significantly reduce the number of examples needed per task compared to learning a single task. The results are demonstrated through theoretical analysis and experimental validation, particularly in the context of feature learning with neural networks. The paper also discusses related work and provides a detailed formal definition of the bias learning model, including definitions of covering numbers and uniform convergence for bias learners.The paper introduces a model for automatically learning inductive bias, which is crucial in machine learning for selecting a hypothesis space that is large enough to contain a solution to the problem but small enough to ensure reliable generalization from limited training data. The model assumes the learner is embedded in an environment of related learning tasks, allowing it to sample from multiple tasks and search for a hypothesis space that performs well on a sufficient number of training tasks. The central assumption is that the learner can improve its performance on novel tasks by learning multiple related tasks within the environment. The paper derives explicit bounds on the sample complexity required for good generalization, showing that learning multiple tasks can significantly reduce the number of examples needed per task compared to learning a single task. The results are demonstrated through theoretical analysis and experimental validation, particularly in the context of feature learning with neural networks. The paper also discusses related work and provides a detailed formal definition of the bias learning model, including definitions of covering numbers and uniform convergence for bias learners.