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Optimal Teaching Curricula with Compositional Simplicity Priors

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Optimal Teaching Curricula with Compositional Simplicity Priors

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García-Piqueras, M.; Hernández-Orallo, J. (2021). Optimal Teaching Curricula with Compositional Simplicity Priors. Springer. 705-721. https://doi.org/10.1007/978-3-030-86486-6_43

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Título: Optimal Teaching Curricula with Compositional Simplicity Priors
Autor: García-Piqueras, Manuel Hernández-Orallo, José
Entidad UPV: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Fecha difusión:
Resumen:
[EN] Machine teaching under strong simplicity priors can teach any concept in universal languages. Remarkably, recent experiments suggest that the teaching sets are shorter than the concept description itself. This raises ...[+]
Palabras clave: Machine teaching , Interposition , Kolmogorov complexity
Derechos de uso: Reserva de todos los derechos
ISBN: 978-3-030-86514-6
Fuente:
Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track. European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings, Part IV. LNCS, volume 12978. (issn: 0302-9743 )
DOI: 10.1007/978-3-030-86486-6_43
Editorial:
Springer
Versión del editor: https://doi.org/10.1007/978-3-030-86486-6_43
Título del congreso: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2021)
Lugar del congreso: Online
Fecha congreso: Septiembre 13-17,2021
Código del Proyecto:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094403-B-C32/ES/RAZONAMIENTO FORMAL PARA TECNOLOGIAS FACILITADORAS Y EMERGENTES/
info:eu-repo/grantAgreement/GVA//PROMETEO%2F2019%2F098//DEEPTRUST/
info:eu-repo/grantAgreement/EC/H2020/952215/EU
Agradecimientos:
This work was funded by the EU (FEDER) and Spanish MINECO under RTI2018-094403-B-C32, G. Valenciana under PROMETEO/2019/098 and EU's Horizon 2020 research and innovation programme under grant 952215 (TAILOR).
Tipo: Comunicación en congreso Artículo Capítulo de libro

References

Antoniol, G., Di Penta, M.: Library miniaturization using static and dynamic information. In: International Conference on Software Maintenance, pp. 235–244 (2003)

Balbach, F.J.: Models for algorithmic teaching. Ph.D. thesis, U. of Lübeck (2007)

Balbach, F.J.: Measuring teachability using variants of the teaching dimension. Theoret. Comput. Sci. 397(1–3), 94–113 (2008) [+]
Antoniol, G., Di Penta, M.: Library miniaturization using static and dynamic information. In: International Conference on Software Maintenance, pp. 235–244 (2003)

Balbach, F.J.: Models for algorithmic teaching. Ph.D. thesis, U. of Lübeck (2007)

Balbach, F.J.: Measuring teachability using variants of the teaching dimension. Theoret. Comput. Sci. 397(1–3), 94–113 (2008)

Brown, T.B., Mann, B., Ryder, N., et al.: Language models are few-shot learners. arXiv:2005.14165 (2020)

Cicalese, F., Laber, E., Molinaro, M., et al.: Teaching with limited information on the learner’s behaviour. In: ICML, pp. 2016–2026. PMLR (2020)

Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv: 1810.04805 (2018)

Elias, P.: Universal codeword sets and representations of the integers. IEEE Trans. Inf. Theory 21(2), 194–203 (1975)

Gao, Z., Ries, C., Simon, H.U., Zilles, S.: Preference-based teaching. J. Mach. Learn. Res. 18(1), 1012–1043 (2017)

Garcia-Piqueras, M., Hernández-Orallo, J.: Conditional teaching size. arXiv: 2107.07038 (2021)

Gong, C.: Exploring commonality and individuality for multi-modal curriculum learning. In: AAAI, vol. 31 (2017)

Gong, C., Yang, J., Tao, D.: Multi-modal curriculum learning over graphs. ACM Trans. Intell. Syst. Technol. (TIST) 10(4), 1–25 (2019)

Gong, T., Zhao, Q., Meng, D., Xu, Z.: Why curriculum learning & self-paced learning work in big/noisy data: a theoretical perspective. BDIA 1(1), 111 (2016)

Gulwani, S., Hernández-Orallo, J., Kitzelmann, E., Muggleton, S.H., Schmid, U., Zorn, B.: Inductive programming meets the real world. Commun. ACM 58(11), 90–99 (2015)

Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., Steinhardt, J.: Measuring massive multitask language understanding. In: ICLR (2021)

Hernández-Orallo, J., Telle, J.A.: Finite and confident teaching in expectation: Sampling from infinite concept classes. In: ECAI (2020)

Kumar, A., Ithapu, V.: A sequential self teaching approach for improving generalization in sound event recognition. In: ICML, pp. 5447–5457 (2020)

Lake, B.M., Salakhutdinov, R., Tenenbaum, J.B.: Human-level concept learning through probabilistic program induction. Science 350(6266), 1332–1338 (2015)

Leibniz, G.W., Rabouin, D.: Mathesis universalis: écrits sur la mathématique universelle. Mathesis (Paris, France) Librairie philosophique J. Vrin (2018)

Li, M., Vitányi, P.M.: An Introduction to Kolmogorov Complexity and Its Applications, 3rd edn. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-030-11298-1

Li, Y., Mao, J., Zhang, X., Freeman, W.T., Tenenbaum, J.B., Wu, J.: Perspective plane program induction from a single image. In: CVPR, pp. 4434–4443 (2020)

Liu, W., et al.: Iterative machine teaching. In: ICML, pp. 2149–2158 (2017)

Manohar, S., Zokaei, N., Fallon, S., Vogels, T., Husain, M.: Neural mechanisms of attending to items in working memory. Neurosci. Biobehav. Rev. 101, 1–12 (2019)

Nye, M.I., Solar-Lezama, A., Tenenbaum, J.B., Lake, B.M.: Learning compositional rules via neural program synthesis. arXiv: 2003.05562 (2020)

Oberauer, K., Lin, H.Y.: An interference model of visual working memory. Psychol. Rev. 124(1), 21 (2017)

Peng, B., Li, C., Li, J., Shayandeh, S., Liden, L., Gao, J.: Soloist: building task bots at scale with transfer learning and machine teaching. arXiv: 2005.05298 (2020)

Pentina, A., Sharmanska, V., Lampert, C.H.: Curriculum learning of multiple tasks. In: Proceedings of Computer Vision and Pattern Recognition (2015)

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019)

Rakhsha, A., Radanovic, G., Devidze, R., Zhu, X., Singla, A.: Policy teaching via environment poisoning: training-time adversarial attacks against reinforcement learning. In: ICML, pp. 7974–7984 (2020)

Salkind, N.: An Introduction to Theories of Human Development. Sage P. (2004)

Schneider, W.X., Albert, J., Ritter, H.: Enabling cognitive behavior of humans, animals, and machines: a situation model framework. ZiF 1, 21–34 (2020)

Shi, Y., Mi, Y., Li, J., Liu, W.: Concept-cognitive learning model for incremental concept learning. IEEE Trans. Syst. Man Cybern. Syst. (2018)

Shindyalov, I., Bourne, P.: Protein structure alignment by incremental combinatorial extension of the optimal path. Prot. Eng. Des. Sel. 11(9), 739–747 (1998)

Shukla, S., et al.: Conversation learner-a machine teaching tool for building dialog managers for task-oriented dialog systems. arXiv: 2004.04305 (2020)

Solomonoff, R.J.: A formal theory of inductive inference I. IC 7(1), 1–22 (1964)

Solomonoff, R.J.: A system for incremental learning based on algorithmic probability. In: Proceedings of the Sixth Israeli Conference on AICVPR, pp. 515–527 (1989)

Soviany, P., Ionescu, R.T., Rota, P., Sebe, N.: Curriculum learning: a survey. arXiv:2101.10382 (2021)

Such, F.P., Rawal, A., Lehman, J., Stanley, K., Clune, J.: Generative teaching networks: accelerating neural architecture search by learning to generate synthetic training data. In: ICML, pp. 9206–9216 (2020)

Telle, J.A., Hernández-Orallo, J., Ferri, C.: The teaching size: computable teachers and learners for universal languages. Mach. Learn. 108(8), 1653–1675 (2019). https://doi.org/10.1007/s10994-019-05821-2

Vygotsky, L.S.: Mind in Society: Development of Higher Psychological Processes. Harvard University Press, Cambridge (1978)

Weinshall, D., Cohen, G., Amir, D.: Curriculum learning by transfer learning: theory and experiments with deep networks. In: ICML, pp. 5235–5243 (2018)

Zhou, T., Bilmes, J.A.: Minimax curriculum learning: machine teaching with desirable difficulties and scheduled diversity. In: ICLR (Poster) (2018)

Zhu, X.: Machine teaching: an inverse problem to machine learning and an approach toward optimal education. In: AAAI, pp. 4083–4087 (2015)

Zhu, X., Singla, A., Zilles, S., Rafferty, A.: An overview of machine teaching. arXiv: 1801.05927 (2018)

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