Learning Set Functions with Implicit Differentiation
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909431365632000 |
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| author | Özcan, Gözde Shi, Chengzhi Ioannidis, Stratis |
| author_facet | Özcan, Gözde Shi, Chengzhi Ioannidis, Stratis |
| contents | Ou et al. (2022) introduce the problem of learning set functions from data generated by a so-called optimal subset oracle. Their approach approximates the underlying utility function with an energy-based model, whose parameters are estimated via mean-field variational inference. Ou et al. (2022) show this reduces to fixed point iterations; however, as the number of iterations increases, automatic differentiation quickly becomes computationally prohibitive due to the size of the Jacobians that are stacked during backpropagation. We address this challenge with implicit differentiation and examine the convergence conditions for the fixed-point iterations. We empirically demonstrate the efficiency of our method on synthetic and real-world subset selection applications including product recommendation, set anomaly detection and compound selection tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11239 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Set Functions with Implicit Differentiation Özcan, Gözde Shi, Chengzhi Ioannidis, Stratis Machine Learning Artificial Intelligence Ou et al. (2022) introduce the problem of learning set functions from data generated by a so-called optimal subset oracle. Their approach approximates the underlying utility function with an energy-based model, whose parameters are estimated via mean-field variational inference. Ou et al. (2022) show this reduces to fixed point iterations; however, as the number of iterations increases, automatic differentiation quickly becomes computationally prohibitive due to the size of the Jacobians that are stacked during backpropagation. We address this challenge with implicit differentiation and examine the convergence conditions for the fixed-point iterations. We empirically demonstrate the efficiency of our method on synthetic and real-world subset selection applications including product recommendation, set anomaly detection and compound selection tasks. |
| title | Learning Set Functions with Implicit Differentiation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2412.11239 |