Information Sharing and Retrieval using
Locally Inferred Probabilistic Models
Paul Blomstedt and Samuel Kaski
Helsinki Institute for Information Technology HIIT,
Department of Computer Science, Aalto University
[email protected]
Probabilistic (a.k.a. Bayesian) machine learning models
• Combine data and domain knowledge to extract inforKnowledge
mation and added value from raw data.
• Particularly useful in problems which require data-efficient
Data +
learning and/or estimates of uncertainty.
=f
• Can be updated as additional knowledge and more data
become available.
Sharing information through models
• Recent advances in distributed probabilistic modeling
g
(e.g. [2]) enable information sharing through models, without the need to disclose private raw data.
• Additional security guarantees can be given using
techniques such as differential privacy [3].
• In large-scale problems, models are used to exchange
information between distributed computational entities
[2, 4].
f1
f2
f3
f4
Retrieval of models
• Content-based information retrieval use measurement
Database
A
A
data instead of meta-data (e.g. key-words) to find relevant data sets in a database.
A,B,C?
Q
• Probabilistic modeling can be used to form informative
representations for retrieval.
C
• The retrieval task then consists in finding relevant mod-
B
els in the database [1].
B
C
References
[1] Blomstedt, P., Dutta, R., Seth, S., Brazma, A. and Kaski, S. Modelling-based experiment retrieval: A case study with gene expression clustering. Bioinformatics, 32(9), 1388–1394, 2016.
[2] Gelman, A., Vehtari, A., Jylänki, P., Sivula, T., Tran, D., Sahai, S., Blomstedt, P., Cunningham, J. P., Schiminovich, D. and Robert, C. Expectation propagation as a way of life: A framework for
Bayesian inference on partitioned data. arXiv preprint, arXiv:1412.4869, 2017.
[3] Heikkilä, M., Okimoto, Y., Kaski, S., Shimizu, K. and Honkela, A. Differentially Private Bayesian Learning on Distributed Data. arXiv preprint, arXiv:1703.01106, 2017.
[4] Qin, X., Blomstedt, P., Leppäaho, E., Parviainen, P. and Kaski, S. Distributed Bayesian Matrix Factorization with Minimal Communication. arXiv preprint, arXiv:1703.00734, 2017.
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