Publications
See DBLP for most of the Bibtex entries.
2022
J. Leslin, A. Hyttinen, K. Periasamy, L. Yao, M. Trapp, M. Andraud
A Hardware Perspective to Evaluating Probabilistic Circuits
11th International Conference on Probabilistic Graphical Models, PGM 2022.
(Article)
A. Hyttinen, V. BarinPacela, A. Hyvärinen
Binary Independent Component Analysis: A Nonstationaritybased Approach
38th Conference on Uncertainty in Artificial Intelligence, UAI 2022.
(Preprint)
(Poster)
2021
S. Tikka, A. Hyttinen, J. Karvanen
Causal Effect Identification from Multiple Incomplete Data Sources: A General Searchbased Approach
Journal of Statistical Software, Articles, Volume 99, Issue 5, 2021.
(Article)
(Preprint)
(Code)
K. Rantanen, A. Hyttinen, M. Järvisalo
Maximal Ancestral Graph Structure Learning via Exact Search
Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021.
(Article)
(Code)
J. Karvanen, S. Tikka, A. Hyttinen
Dosearch  a tool for causal inference and study design with multiple data sources
Epidemiology (journal), Volume 32, Issue 1, p. 111119, 2021.
(Article)
(Preprint)
(Code)
2020
J. Viinikka, A. Hyttinen, J. Pensar, M. Koivisto
Towards Scalable Bayesian Learning of Causal DAGs
Advances in Neural Information Processing Systems 33, NeurIPS 2020.
(Article)
(Preprint)
(Supplement)
(Code)
R. Laine, A. Hyttinen, M. Mathioudakis
Evaluating Decision Makers over Selectively Labeled Data: A Causal Modelling Approach
23rd International Conference on Discovery Science, DS 2020.
Best Paper Award.
(Article)
(Code)
K. Rantanen, A. Hyttinen, M. Järvisalo
Learning Optimal Cyclic Causal Graphs from Interventional Data
The 10th International Conference on Probabilistic Graphical Models, PGM 2020.
(Article)
(Code)
K. Rantanen, A. Hyttinen, M. Järvisalo
Learning Chordal Markov Networks via Stochastic Local Search
Proceedings of the 24th European Conference on Artificial Intelligence, ECAI 2020.
(Article)
(Code)
J. Pensar, T. Talvitie, A. Hyttinen, M. Koivisto
A Bayesian Approach for Estimating Causal Effects from Observational Data
Proceedings of the ThirtyFourth AAAI Conference on Artificial Intelligence, AAAI 2020.
(Article)
(Code)
K. Rantanen, A. Hyttinen, M. Järvisalo
Discovering Causal Graphs with Cycles and Latent Confounders: An Exact BranchandBound Approach
International Journal of Approximate Reasoning, Volume 117, Feb. 2020.
(Article)
(Code)
2019
S. Tikka, A. Hyttinen, J. Karvanen
Identifying Causal Effects via Contextspecific Independence Relations
Advances in Neural Information Processing Systems 32, NeurIPS 2019.
(Article)
(Code)
J. Corander, A. Hyttinen, J. Kontinen, J. Pensar, J. Väänänen
A Logical Approach to ContextSpecific Independence
Annals of Pure and Applied Logic, 2019.
(Article)
(Implementation)
J. Berg, A. Hyttinen, and M. Järvisalo
Applications of MaxSAT in Data Analysis
EPiC Series in Computing, volume 59, Proceedings of Pragmatics of SAT 2015 and 2018, p. 50 64, Easychair, 2019.
(Article)
(Benchmarks)
2018
K. Rantanen, A. Hyttinen, M. Järvisalo
Learning Optimal Causal Graphs with Exact Search
The 9th International Conference on Probabilistic Graphical Models, PGM 2018.
Best Student Paper Award.
(Article)
(Code)
A. Hyttinen, J. Pensar, J. Kontinen, J. Corander
Structure Learning for Bayesian Networks over Labeled DAGs
The 9th International Conference on Probabilistic Graphical Models, PGM 2018.
(Article)
(Presentation)
F. Bacchus, A. Hyttinen, M. Järvisalo, and P. Saikko
Reduced Cost Fixing for Maximum Satisfiability
International Joint Conference on Artificial Intelligence,
Best Sister Conferences, IJCAI 2018.
(Article)
(Implemented e.g. in the MaxHS solver)
2017
K. Rantanen, A. Hyttinen, M. Järvisalo
Learning Chordal Markov Networks via Branch and Bound
Advances in Neural Information Processing Systems 30, NIPS 2017.
(Article)
(Code)
A. Hyttinen, S. Plis, M. Järvisalo, F. Eberhardt, and D. Danks
A Constraint Optimization
Approach to Causal Discovery from Subsampled Time
Series Data
International Journal of Approximate Reasoning, 2017.
(Article)
(Presentation)
(Code)
F. Bacchus, A. Hyttinen, M. Järvisalo, and P. Saikko
Reduced Cost Fixing in MaxSAT
The 23rd International Conference on Principles and Practice of Constraint Programming, CP 2017.
Distinguished Paper Award.
(Article)
(Implemented e.g. in the MaxHS solver)
A. Hyttinen, P. Saikko, and M. Järvisalo
A CoreGuided Approach to Learning Optimal Causal Graphs
International Joint Conference on Artificial Intelligence, IJCAI
2017.
(Article)
(Presentation)
(Appendix)
(Benchmarks)
2016 and earlier
A. Hyttinen, S. Plis, M. Järvisalo, F. Eberhardt, and
D. Danks
Causal Discovery from Subsampled Time Series Data by Constraint Optimization
Proceedings of the Eight International Conference on
Probabilistic Graphical Models, PGM 2016.
(Article)
(Presentation)
(Code)
J. Corander, A. Hyttinen, J. Kontinen, J. Pensar, and J. Väänänen
A logical approach to contextspecific independence
WoLLIC 2016, 23rd Workshop on Logic, Language, Information and Computation, 2016.
(Article)
A. Hyttinen, F. Eberhardt, and M. Järvisalo
Docalculus when the True Graph is Unknown
Proceedings of the 31th Conference on Uncertainty in Artificial Intelligence, UAI 2015.
(Article)
(Code)
D. Sonntag, M. Järvisalo, Jose M. Pena, A. Hyttinen
Learning Optimal Chain Graphs with Answer Set Programming
Proceedings of the 31th Conference on Uncertainty in Artificial Intelligence, UAI 2015.
(Article) (Code included in the article.)
A. Hyttinen, F. Eberhardt, and M. Järvisalo
Constraintbased Causal Discovery: Conflict Resolution with Answer Set Programming
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, UAI 2014.
(Article)
(Supplement)
(Code)
A. Hyttinen, P. O. Hoyer, F. Eberhardt, and M. Järvisalo
Discovering Cyclic Causal Models with Latent Variables: A General SATBased Procedure
Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, UAI 2013.
(Article)
(Presentation)
(Code)
A. Hyttinen
Discovering Causal Relations in the Presence of Latent Confounders
Ph.D. Thesis, May, 2013.
The thesis consists of an intro part and the
previous six research articles, printed in their
original form.
(Intro)
A. Hyttinen, F. Eberhardt, and P. O. Hoyer
Experiment Selection for Causal Discovery
Journal of Machine Learning Research 14(Oct):3041−3071, 2013.
(Article)
(Code)
A. Hyttinen, F. Eberhardt, and P. O. Hoyer
Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables
Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, UAI 2012.
(Article)
(Appendix)
(Presentation)
(Code)
A. Hyttinen, F. Eberhardt, and P. O. Hoyer
Learning Linear Cyclic Causal Models with Latent Variables
Journal of Machine Learning Research, 13(Nov):33873439, 2012.
(Article)
(Code)
A. Hyttinen, F. Eberhardt, and P. O. Hoyer
NoisyOR Models with Latent Confounding
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011.
(Article)
(Poster)
(Code)
A.Hyttinen, F. Eberhardt, and P. O. Hoyer
Causal discovery for linear cyclic models with
latent variables
Proceedings of the Fifth European Workshop on Probabilistic Graphical Models, Helsinki, PGM 2010.
(Article)
(Appendix)
(Presentation)
(Poster)
(Code)
P. O. Hoyer and A. Hyttinen
Bayesian Discovery of Linear Acyclic Causal Models
Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009.
(Article)
(Poster)
(Code)
