Optimal Cooperative Inference

Scott Cheng-Hsin Yang, Yue Yu, arash Givchi, Pei Wang, Wai Keen Vong, Patrick Shafto
Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, PMLR 84:376-385, 2018.

Abstract

Cooperative transmission of data fosters rapid accumulation of knowledge by efficiently combining experiences across learners. Although well studied in human learning and increasingly in machine learning, we lack formal frameworks through which we may reason about the benefits and limitations of cooperative inference. We present such a framework. We introduce novel indices for measuring the effectiveness of probabilistic and cooperative information transmission. We relate our indices to the well-known Teaching Dimension in deterministic settings. We prove conditions under which optimal cooperative inference can be achieved, including a representation theorem that constrains the form of inductive biases for learners optimized for cooperative inference. We conclude by demonstrating how these principles may inform the design of machine learning algorithms and discuss implications for human and machine learning.

Cite this Paper

BibTeX
@InProceedings{pmlr-v84-yang18a, title = {Optimal Cooperative Inference}, author = {Yang, Scott Cheng-Hsin and Yu, Yue and Givchi, arash and Wang, Pei and Vong, Wai Keen and Shafto, Patrick}, booktitle = {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics}, pages = {376--385}, year = {2018}, editor = {Storkey, Amos and Perez-Cruz, Fernando}, volume = {84}, series = {Proceedings of Machine Learning Research}, month = {09--11 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v84/yang18a/yang18a.pdf}, url = {https://proceedings.mlr.press/v84/yang18a.html}, abstract = {Cooperative transmission of data fosters rapid accumulation of knowledge by efficiently combining experiences across learners. Although well studied in human learning and increasingly in machine learning, we lack formal frameworks through which we may reason about the benefits and limitations of cooperative inference. We present such a framework. We introduce novel indices for measuring the effectiveness of probabilistic and cooperative information transmission. We relate our indices to the well-known Teaching Dimension in deterministic settings. We prove conditions under which optimal cooperative inference can be achieved, including a representation theorem that constrains the form of inductive biases for learners optimized for cooperative inference. We conclude by demonstrating how these principles may inform the design of machine learning algorithms and discuss implications for human and machine learning. } }
Endnote
%0 Conference Paper %T Optimal Cooperative Inference %A Scott Cheng-Hsin Yang %A Yue Yu %A arash Givchi %A Pei Wang %A Wai Keen Vong %A Patrick Shafto %B Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2018 %E Amos Storkey %E Fernando Perez-Cruz %F pmlr-v84-yang18a %I PMLR %P 376--385 %U https://proceedings.mlr.press/v84/yang18a.html %V 84 %X Cooperative transmission of data fosters rapid accumulation of knowledge by efficiently combining experiences across learners. Although well studied in human learning and increasingly in machine learning, we lack formal frameworks through which we may reason about the benefits and limitations of cooperative inference. We present such a framework. We introduce novel indices for measuring the effectiveness of probabilistic and cooperative information transmission. We relate our indices to the well-known Teaching Dimension in deterministic settings. We prove conditions under which optimal cooperative inference can be achieved, including a representation theorem that constrains the form of inductive biases for learners optimized for cooperative inference. We conclude by demonstrating how these principles may inform the design of machine learning algorithms and discuss implications for human and machine learning.
APA
Yang, S.C., Yu, Y., Givchi, a., Wang, P., Vong, W.K. & Shafto, P.. (2018). Optimal Cooperative Inference. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 84:376-385 Available from https://proceedings.mlr.press/v84/yang18a.html.

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