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2020 – today
- 2022
[c26]Jelin Leslin, Antti Hyttinen, Karthekeyan Periasamy, Lingyun Yao, Martin Trapp, Martin Andraud:
A Hardware Perspective to Evaluating Probabilistic Circuits. PGM 2022: 349-360- [c25]Antti Hyttinen, Vitória Barin Pacela
, Aapo Hyvärinen:
Binary independent component analysis: a non-stationarity-based approach. UAI 2022: 874-884 - 2021
- [j6]Santtu Tikka, Antti Hyttinen, Juha Karvanen:
Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-Based Approach. J. Stat. Softw. 99(5) (2021) - [c24]Kari Rantanen, Antti Hyttinen, Matti Järvisalo:
Maximal ancestral graph structure learning via exact search. UAI 2021: 1237-1247 - [i9]Antti Hyttinen, Vitória Barin Pacela, Aapo Hyvärinen:
Binary Independent Component Analysis via Non-stationarity. CoRR abs/2111.15431 (2021) - 2020
- [j5]Kari Rantanen, Antti Hyttinen, Matti Järvisalo:
Discovering causal graphs with cycles and latent confounders: An exact branch-and-bound approach. Int. J. Approx. Reason. 117: 29-49 (2020) - [c23]Johan Pensar, Topi Talvitie, Antti Hyttinen, Mikko Koivisto:
A Bayesian Approach for Estimating Causal Effects from Observational Data. AAAI 2020: 5395-5402 - [c22]Riku Laine, Antti Hyttinen, Michael Mathioudakis:
Evaluating Decision Makers over Selectively Labelled Data: A Causal Modelling Approach. DS 2020: 3-18 - [c21]Kari Rantanen, Antti Hyttinen, Matti Järvisalo:
Learning Chordal Markov Networks via Stochastic Local Search. ECAI 2020: 2632-2639 - [c20]Jussi Viinikka, Antti Hyttinen, Johan Pensar, Mikko Koivisto:
Towards Scalable Bayesian Learning of Causal DAGs. NeurIPS 2020 - [c19]Kari Rantanen, Antti Hyttinen, Matti Järvisalo:
Learning Optimal Cyclic Causal Graphs from Interventional Data. PGM 2020: 365-376 - [i8]Santtu Tikka, Antti Hyttinen, Juha Karvanen:
Identifying Causal Effects via Context-specific Independence Relations. CoRR abs/2009.09768 (2020) - [i7]Jussi Viinikka, Antti Hyttinen, Johan Pensar, Mikko Koivisto:
Towards Scalable Bayesian Learning of Causal DAGs. CoRR abs/2010.00684 (2020)
2010 – 2019
- 2019
- [j4]Jukka Corander, Antti Hyttinen, Juha Kontinen, Johan Pensar, Jouko Väänänen:
A logical approach to context-specific independence. Ann. Pure Appl. Log. 170(9): 975-992 (2019) - [c18]Santtu Tikka, Antti Hyttinen, Juha Karvanen:
Identifying Causal Effects via Context-specific Independence Relations. NeurIPS 2019: 2800-2810 - [i6]Santtu Tikka, Antti Hyttinen, Juha Karvanen:
Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach. CoRR abs/1902.01073 (2019) - 2018
- [c17]Fahiem Bacchus, Antti Hyttinen, Matti Järvisalo, Paul Saikko:
Reduced Cost Fixing for Maximum Satisfiability. IJCAI 2018: 5209-5213 - [c16]Antti Hyttinen, Johan Pensar, Juha Kontinen, Jukka Corander:
Structure Learning for Bayesian Networks over Labeled DAGs. PGM 2018: 133-144 - [c15]Kari Rantanen, Antti Hyttinen, Matti Järvisalo:
Learning Optimal Causal Graphs with Exact Search. PGM 2018: 344-355 - [c14]Jeremias Berg, Antti Hyttinen, Matti Järvisalo:
Applications of MaxSAT in Data Analysis. POS@SAT 2018: 50-64 - 2017
- [j3]Antti Hyttinen, Sergey M. Plis, Matti Järvisalo, Frederick Eberhardt, David Danks:
A constraint optimization approach to causal discovery from subsampled time series data. Int. J. Approx. Reason. 90: 208-225 (2017) - [c13]Joe Suzuki, Antti Hyttinen, Brandon M. Malone:
Advanced Methodologies for Bayesian Networks 2017: Preface. AMBN 2017: 1-2 - [c12]Fahiem Bacchus, Antti Hyttinen, Matti Järvisalo, Paul Saikko:
Reduced Cost Fixing in MaxSAT. CP 2017: 641-651 - [c11]Antti Hyttinen, Paul Saikko, Matti Järvisalo:
A Core-Guided Approach to Learning Optimal Causal Graphs. IJCAI 2017: 645-651 - [c10]Kari Rantanen, Antti Hyttinen, Matti Järvisalo:
Learning Chordal Markov Networks via Branch and Bound. NIPS 2017: 1847-1857 - [e2]Antti Hyttinen, Joe Suzuki, Brandon M. Malone:
Proceedings of the 3rd Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2017, Kyoto, Japan, September 20-22, 2017. Proceedings of Machine Learning Research 73, PMLR 2017 [contents] - 2016
- [c9]Antti Hyttinen, Sergey M. Plis, Matti Järvisalo, Frederick Eberhardt, David Danks:
Causal Discovery from Subsampled Time Series Data by Constraint Optimization. Probabilistic Graphical Models 2016: 216-227 - [c8]Jukka Corander, Antti Hyttinen, Juha Kontinen, Johan Pensar, Jouko Väänänen:
A Logical Approach to Context-Specific Independence. WoLLIC 2016: 165-182 - [i5]Antti Hyttinen, Sergey M. Plis, Matti Järvisalo, Frederick Eberhardt, David Danks:
Causal Discovery from Subsampled Time Series Data by Constraint Optimization. CoRR abs/1602.07970 (2016) - 2015
- [c7]Antti Hyttinen, Frederick Eberhardt, Matti Järvisalo:
Do-calculus when the True Graph Is Unknown. UAI 2015: 395-404 - [c6]Dag Sonntag, Matti Järvisalo, José M. Peña, Antti Hyttinen:
Learning Optimal Chain Graphs with Answer Set Programming. UAI 2015: 822-831 - 2014
- [c5]Antti Hyttinen, Frederick Eberhardt, Matti Järvisalo:
Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming. UAI 2014: 340-349 - [e1]Joris M. Mooij, Dominik Janzing, Jonas Peters, Tom Claassen, Antti Hyttinen:
Proceedings of the UAI 2014 Workshop Causal Inference: Learning and Prediction co-located with 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014), Quebec City, Canada, July 27, 2014. CEUR Workshop Proceedings 1274, CEUR-WS.org 2014 [contents] - 2013
- [b1]Antti Hyttinen:
Discovering Causal Relations in the Presence of Latent Confounders. University of Helsinki, Finland, 2013 - [j2]Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer:
Experiment selection for causal discovery. J. Mach. Learn. Res. 14(1): 3041-3071 (2013) - [c4]Antti Hyttinen, Patrik O. Hoyer, Frederick Eberhardt, Matti Järvisalo:
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure. UAI 2013 - [i4]Antti Hyttinen, Patrik O. Hoyer, Frederick Eberhardt, Matti Järvisalo:
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure. CoRR abs/1309.6836 (2013) - 2012
- [j1]Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer:
Learning linear cyclic causal models with latent variables. J. Mach. Learn. Res. 13: 3387-3439 (2012) - [c3]Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer:
Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables. UAI 2012: 387-396 - [i3]Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer:
Noisy-OR Models with Latent Confounding. CoRR abs/1202.3735 (2012) - [i2]Patrik O. Hoyer, Antti Hyttinen:
Bayesian Discovery of Linear Acyclic Causal Models. CoRR abs/1205.2641 (2012) - [i1]Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer:
Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables. CoRR abs/1210.4879 (2012) - 2011
- [c2]Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer:
Noisy-OR Models with Latent Confounding. UAI 2011: 363-372
2000 – 2009
- 2009
- [c1]Patrik O. Hoyer, Antti Hyttinen:
Bayesian Discovery of Linear Acyclic Causal Models. UAI 2009: 240-248
Coauthor Index
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