Antti Hyttinen

Title of Docent, Department of Computer Science
University Researcher, Department of Computer Science
Postdoctoral Researcher, Helsinki Institute for Information Technology

Phone+358407525515, +358294151164, 0294151164, 0407525515
Emailantti.hyttinen@helsinki.fi

PL 68 (Pietari Kalmin katu 5)
00014
Finland
PL 68 (Pietari Kalmin katu 5), A323
HELSINGIN YLIOPISTO
Finland

Research activity per year

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20 Conference contribution
6 Article
1 Software
1 Doctoral Thesis

28 results

2021

Do-search A Tool for Causal Inference and Study Design with Multiple Data Sources
Karvanen, J., Tikka, S. & Hyttinen, A., Jan 2021, In : Epidemiology. 32, 1, p. 111-119 9 p.
Research output: Contribution to journal › Article › Scientific › peer-review
2020

Discovering Causal Graphs with Cycles and Latent Confounders: An Exact Branch-and-Bound Approach
Rantanen, K., Hyttinen, A. & Järvisalo, M., Feb 2020, In : International Journal of Approximate Reasoning. 117, p. 29-49 21 p.
Research output: Contribution to journal › Article › Scientific › peer-review
Evaluating Decision Makers over Selectively Labelled Data: A Causal Modelling Approach
Laine, R., Hyttinen, A. & Mathioudakis, M., 2020, Discovery Science: DS 2020. Cham: Springer, p. 3-18 16 p. (Lecture Notes in Computer Science; vol. 12323).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Learning Chordal Markov Networks via Stochastic Local Search
Rantanen, K., Hyttinen, A. & Järvisalo, M., 2020, ECAI 2020: 24th European conference on artificial intelligence. De Giacomo, G., Catala, A., Dilkina, B., Milano, M., Barro, S., Bugarín, A. & Lang, J. (eds.). IOS PRESS, p. 2632-2639 8 p. (Frontiers in Artificial Intelligence and Applications; vol. 325).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Open Access
File
Learning Optimal Cyclic Causal Graphs from Interventional Data
Rantanen, K., Hyttinen, A. & Järvisalo, M., 2020, Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020). Jaeger, M. & Nielsen, T. D. (eds.). Journal of Machine Learning Research, p. 365-376 12 p. (Proceedings of Machine Learning Research; vol. 138).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Open Access
File
2019

A Logical Approach to Context-Specific Independence
Corander, J., Hyttinen, A., Kontinen, J., Pensar, J. & Väänänen, J., Sep 2019, In : Annals of Pure and Applied Logic. 170, 9, p. 975-992 18 p.
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
File
Applications of MaxSAT in Data Analysis
Berg, O. J., Hyttinen, A. J. & Järvisalo, M. J., 2019, Proceedings of Pragmatics of SAT 2015 and 2018. Le Berre, D. & Järvisalo, M. (eds.). Manchester, UK: EasyChair Publications, p. 50-64 15 p. (EPiC Series in Computing; vol. 59).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Open Access
File
Identifying Causal Effects via Context-specific Independence Relations
Tikka, S., Hyttinen, A. & Karvanen, J., 2019, Advances in Neural Information Processing Systems 32 (NIPS 2019). Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E. & Garnett, R. (eds.). Maryland Heights, MO: Morgan Kaufmann Publishers, 11 p. (Advances in Neural Information Processing Systems; vol. 32).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Open Access
File
2018

Learning Optimal Causal Graphs with Exact Search
Rantanen, K. M. J., Hyttinen, A. J. & Järvisalo, M. J., 2018, Proceedings of the Ninth International Conference on Probabilistic Graphical Models. Kratochvíl, V. & Studený, M. (eds.). Cambridge, MA: Journal of Machine Learning Research, p. 344-355 12 p. (Proceedings of Machine Learning Research; vol. 72).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Open Access
File
Reduced Cost Fixing for Maximum Satisfiability
Bacchus, F., Hyttinen, A. J., Järvisalo, M. J. & Saikko, P. H. A., 2018, Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018). Lang, J. (ed.). Palo Alto, California: AAAI Press, p. 5209-5213 5 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Open Access
File
Structure Learning for Bayesian Networks over Labeled DAGs
Hyttinen, A., Pensar, J., Kontinen, J. & Corander, J., 2018, Proceedings of the Ninth International Conference on Probabilistic Graphical Models. PMLR, Vol. 72. p. 133-144 12 p. (Proceedings of Machine Learning Research; vol. 72).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Open Access
File
2017

A Constraint Optimization Approach to Causal Discovery from Subsampled Time Series Data
Hyttinen, A., Plis, S., Järvisalo, M., Eberhardt, F. & Danks, D., Nov 2017, In : International Journal of Approximate Reasoning. 90, p. 208-225 18 p.
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
File
A Core-Guided Approach to Learning Optimal Causal Graphs
Hyttinen, A., Saikko, P. & Järvisalo, M., 2017, Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017). Sierra, C. (ed.). International Joint Conferences on Artificial Intelligence, p. 645-651 7 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Open Access
File
BBMarkov: a branch-and-bound approach to learning optimal decomposable graphs
Rantanen, K. M. J., Hyttinen, A. J. & Järvisalo, M. J., 2017
Research output: Non-textual form › Software › Scientific
Learning Chordal Markov Networks via Branch and Bound
Rantanen, K., Hyttinen, A. & Järvisalo, M., 2017, Advances in Neural Information Processing Systems 30 (NIPS 2017). Guyon, I. (ed.). Neural Information Processing Systems Foundation, 11 p. (Advances in neural information processing systems; vol. 30).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Open Access
File
Reduced Cost Fixing in MaxSAT
Bacchus, F., Hyttinen, A., Järvisalo, M. & Saikko, P., 2017, Principles and Practice of Constraint Programming: 23rd International Conference, CP 2017 Melbourne, VIC, Australia, August 28 – September 1, 2017 Proceedings. Beck, J. C. (ed.). Cham: Springer International Publishing AG, p. 641-651 11 p. (Lecture Notes in Computer Science; vol. 10416).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Open Access
File
2016

A Logical Approach to Context-Specific Independence
Corander, J., Hyttinen, A., Kontinen, J., Pensar, J. & Väänänen, J., Aug 2016, Logic, Language, Information, and Computation: 23rd International Workshop, WoLLIC 2016, Puebla, Mexico, August 16-19th, 2016. Proceedings. Berlin: Springer-Verlag, p. 165-182 18 p. (Lecture Notes in Computer Science; vol. 9803).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Causal Discovery from Subsampled Time Series Data by Constraint Optimization
Hyttinen, A. J., Plis, S. M., Järvisalo, M. J., Eberhardt, F. & Danks, D., 2016, Proceedings of the 8th International Conference on Probabilistic Graphical Models (PGM 2016). Antonucci, A., Corani, G. & de Campos, C. P. (eds.). Journal of Machine Learning Research, Vol. 52. p. 216-227 12 p. (JMLR Workshop and Conference Proceedings).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Open Access
2015

Do-calculus when the True Graph is Unknown
Hyttinen, A., Eberhardt, F. & Järvisalo, M., 2015, Uncertainty in Artificial Intelligence : Proceedings of the Thirty-First Conference (2015) : July 12‑16, 2015, Amsterdam, Netherlands. Meila, M. & Heskes, T. (eds.). Corvallis, OR: AUAI Press, p. 395-404 10 p. (Uncertainty in artificial intelligence).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Open Access
Learning Optimal Chain Graphs with Answer Set Programming
Sonntag, D., Järvisalo, M. J., Peña, J. & Hyttinen, A., 2015, Conference on Uncertainty in Artificial Intelligence : Proceedings of the Thirty‑First Conference (2015) : July 12‑16, 2015, Amsterdam, Netherlands. Heskes, T. & Meila, M. (eds.). Corvallis, Ore: AUAI Press, p. 822-831 10 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Open Access
2013

Discovering Causal Relations in the Presence of Latent Confounders
Hyttinen, A., 2013, Helsinki: University of Helsinki, Department of Computer Science. 245 p.
Research output: Thesis › Doctoral Thesis › Collection of Articles
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure
Hyttinen, A., Hoyer, P., Ederhardt, F. & Järvisalo, M., 2013, Uncertainty In Artificial Intelligence: Proceedings of the Twenty-Ninth Conference (2013). Nicholson, A. & Smyth, P. (eds.). Oregon: AUAI Press, p. 301-310 10 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Experiment Selection for Causal Discovery
Hyttinen, A., Eberhardt, F. & Hoyer, P. O., Oct 2013, In : Journal of Machine Learning Research. 2013, 14, p. 3041-3071 31 p.
Research output: Contribution to journal › Article › Scientific › peer-review
2012

Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables
Hyttinen, A., Eberhardt, F. & Hoyer, P., 2012, Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence 2012. 10 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Learning Linear Cyclic Causal Models with Latent Variables
Hyttinen, A., Eberhardt, F. & Hoyer, P. O., Nov 2012, In : Journal of Machine Learning Research. 13, p. 3387-3439 53 p.
Research output: Contribution to journal › Article › Scientific › peer-review
2011

Noisy-OR Models with Latent Confounding
Hyttinen, A., Eberhardt, F. & Hoyer, P., 2011, Uncertainty in artificial intelligence: Proceedings of the twenty-seventh conference (2011). Cozman, F. G. & Pfeffer, A. (eds.). AUAI Press, p. 363-372 10 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
2010

Causal discovery for linear cyclic models with latent variables
Hyttinen, A., Eberhardt, F. & Hoyer, P. O., 2010, Proceedings of the Fifth European Workshop on Probabilistic Graphical Models: PGM 2010. Myllymäki, P., Roos, T. & Jaakkola, T. (eds.). Helsinki: Helsinki Institute for Information Technology HIIT, p. 153-160 [163-170] 8 p. (HIIT Publications; vol. 2010, no. 2).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
2009

Bayesian discovery of linear acyclic causal models
Hoyer, P. O. & Hyttinen, A., 2009, UAI '09: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence . Arlington, VA: AUAI Press, p. 240-248 9 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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