Research output per year
Research output per year
- 23 results
Search results
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2022
Binary Independent Component Analysis: A Non-stationarity-based Approach
Hyttinen, A., Barin-Pacela, V. & Hyvärinen, A., 2022, The 38th Conference on Uncertainty in Artificial Intelligence. The Association for Uncertainty in Artificial Intelligence, p. 874-884 11 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
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2021
Maximal Ancestral Graph Structure Learning via Exact Search
Rantanen, K., Hyttinen, A. & Järvisalo, M., 2021, Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021). de Campos, C. P., Maathuis, M. H. & Quaeghebeur, E. (eds.). AUAI Press, p. 1237-1247 11 p. (Proceedings of Machine Learning Research; vol. 161).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Open AccessFile -
2020
A Bayesian Approach for Estimating Causal Effects from Observational Data
Pensar, J., Talvitie, T., Hyttinen, A. & Koivisto, M., 2020, THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE. The Association for the Advancement of Artificial Intelligence (AAAI), p. 5395-5402 8 p. (AAAI Conference on Artificial Intelligence; vol. 34).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Open AccessFile -
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
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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
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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 AccessFile -
2019
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
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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 AccessFile -
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
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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 AccessFile -
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 AccessFile -
2017
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 AccessFile -
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 AccessFile -
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 AccessFile -
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
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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 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
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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
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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
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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
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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