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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2109.14501 |
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| _version_ | 1866929376416759808 |
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| author | Dey, Jayanta Geisa, Ali Mehta, Ronak Tomita, Tyler M. Helm, Hayden S. Xu, Haoyin Eaton, Eric Dick, Jeffery Priebe, Carey E. Vogelstein, Joshua T. |
| author_facet | Dey, Jayanta Geisa, Ali Mehta, Ronak Tomita, Tyler M. Helm, Hayden S. Xu, Haoyin Eaton, Eric Dick, Jeffery Priebe, Carey E. Vogelstein, Joshua T. |
| contents | Learning is a process wherein a learning agent enhances its performance through exposure of experience or data. Throughout this journey, the agent may encounter diverse learning environments. For example, data may be presented to the leaner all at once, in multiple batches, or sequentially. Furthermore, the distribution of each data sample could be either identical and independent (iid) or non-iid. Additionally, there may exist computational and space constraints for the deployment of the learning algorithms. The complexity of a learning task can vary significantly, depending on the learning setup and the constraints imposed upon it. However, it is worth noting that the current literature lacks formal definitions for many of the in-distribution and out-of-distribution learning paradigms. Establishing proper and universally agreed-upon definitions for these learning setups is essential for thoroughly exploring the evolution of ideas across different learning scenarios and deriving generalized mathematical bounds for these learners. In this paper, we aim to address this issue by proposing a chronological approach to defining different learning tasks using the provably approximately correct (PAC) learning framework. We will start with in-distribution learning and progress to recently proposed lifelong or continual learning. We employ consistent terminology and notation to demonstrate how each of these learning frameworks represents a specific instance of a broader, more generalized concept of learnability. Our hope is that this work will inspire a universally agreed-upon approach to quantifying different types of learning, fostering greater understanding and progress in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2109_14501 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Towards a theory of out-of-distribution learning Dey, Jayanta Geisa, Ali Mehta, Ronak Tomita, Tyler M. Helm, Hayden S. Xu, Haoyin Eaton, Eric Dick, Jeffery Priebe, Carey E. Vogelstein, Joshua T. Machine Learning Artificial Intelligence Learning is a process wherein a learning agent enhances its performance through exposure of experience or data. Throughout this journey, the agent may encounter diverse learning environments. For example, data may be presented to the leaner all at once, in multiple batches, or sequentially. Furthermore, the distribution of each data sample could be either identical and independent (iid) or non-iid. Additionally, there may exist computational and space constraints for the deployment of the learning algorithms. The complexity of a learning task can vary significantly, depending on the learning setup and the constraints imposed upon it. However, it is worth noting that the current literature lacks formal definitions for many of the in-distribution and out-of-distribution learning paradigms. Establishing proper and universally agreed-upon definitions for these learning setups is essential for thoroughly exploring the evolution of ideas across different learning scenarios and deriving generalized mathematical bounds for these learners. In this paper, we aim to address this issue by proposing a chronological approach to defining different learning tasks using the provably approximately correct (PAC) learning framework. We will start with in-distribution learning and progress to recently proposed lifelong or continual learning. We employ consistent terminology and notation to demonstrate how each of these learning frameworks represents a specific instance of a broader, more generalized concept of learnability. Our hope is that this work will inspire a universally agreed-upon approach to quantifying different types of learning, fostering greater understanding and progress in the field. |
| title | Towards a theory of out-of-distribution learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2109.14501 |