Antti Hyttinen

Publication Activity (10 Years)

Years Active: 2009-2022
Publications (10 Years): 28

Top Topics

Causal Discovery

Answer Set Programming

Constraint Optimization

Observational Data

Top Venues

Int. J. Approx. Reason.

Publications

Antti Hyttinen, Vitória Barin Pacela, Aapo Hyvärinen
Binary independent component analysis: a non-stationarity-based approach. UAI (2022)
Jelin Leslin, Antti Hyttinen, Karthekeyan Periasamy, Lingyun Yao, Martin Trapp, Martin Andraud
A Hardware Perspective to Evaluating Probabilistic Circuits. PGM (2022)
Antti Hyttinen, Vitória Barin Pacela, Aapo Hyvärinen
Binary Independent Component Analysis via Non-stationarity. CoRR (2021)
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)
Kari Rantanen, Antti Hyttinen, Matti Järvisalo
Maximal ancestral graph structure learning via exact search. UAI (2021)
Santtu Tikka, Antti Hyttinen, Juha Karvanen
Identifying Causal Effects via Context-specific Independence Relations. CoRR (2020)
Jussi Viinikka, Antti Hyttinen, Johan Pensar, Mikko Koivisto
Towards Scalable Bayesian Learning of Causal DAGs. NeurIPS (2020)
Kari Rantanen, Antti Hyttinen, Matti Järvisalo
Learning Chordal Markov Networks via Stochastic Local Search. ECAI (2020)
Riku Laine, Antti Hyttinen, Michael Mathioudakis
Evaluating Decision Makers over Selectively Labelled Data: A Causal Modelling Approach. DS (2020)
Johan Pensar, Topi Talvitie, Antti Hyttinen, Mikko Koivisto
A Bayesian Approach for Estimating Causal Effects from Observational Data. AAAI (2020)
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 (2020)
Jussi Viinikka, Antti Hyttinen, Johan Pensar, Mikko Koivisto
Towards Scalable Bayesian Learning of Causal DAGs. CoRR (2020)
Kari Rantanen, Antti Hyttinen, Matti Järvisalo
Learning Optimal Cyclic Causal Graphs from Interventional Data. PGM (2020)
Santtu Tikka, Antti Hyttinen, Juha Karvanen
Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach. CoRR (2019)
Jukka Corander, Antti Hyttinen, Juha Kontinen, Johan Pensar, Jouko Väänänen
A logical approach to context-specific independence. Ann. Pure Appl. Log. 170 (9) (2019)
Santtu Tikka, Antti Hyttinen, Juha Karvanen
Identifying Causal Effects via Context-specific Independence Relations. NeurIPS (2019)
Antti Hyttinen, Johan Pensar, Juha Kontinen, Jukka Corander
Structure Learning for Bayesian Networks over Labeled DAGs. PGM (2018)
Fahiem Bacchus, Antti Hyttinen, Matti Järvisalo, Paul Saikko
Reduced Cost Fixing for Maximum Satisfiability. IJCAI (2018)
Jeremias Berg, Antti Hyttinen, Matti Järvisalo
Applications of MaxSAT in Data Analysis. POS@SAT (2018)
Kari Rantanen, Antti Hyttinen, Matti Järvisalo
Learning Optimal Causal Graphs with Exact Search. PGM (2018)
Kari Rantanen, Antti Hyttinen, Matti Järvisalo
Learning Chordal Markov Networks via Branch and Bound. NIPS (2017)
Antti Hyttinen, Paul Saikko, Matti Järvisalo
A Core-Guided Approach to Learning Optimal Causal Graphs. IJCAI (2017)
Joe Suzuki, Antti Hyttinen, Brandon M. Malone
Advanced Methodologies for Bayesian Networks 2017: Preface. AMBN (2017)
Fahiem Bacchus, Antti Hyttinen, Matti Järvisalo, Paul Saikko
Reduced Cost Fixing in MaxSAT. CP (2017)
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 (2017)
Jukka Corander, Antti Hyttinen, Juha Kontinen, Johan Pensar, Jouko Väänänen
A Logical Approach to Context-Specific Independence. WoLLIC (2016)
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)
Antti Hyttinen, Sergey M. Plis, Matti Järvisalo, Frederick Eberhardt, David Danks
Causal Discovery from Subsampled Time Series Data by Constraint Optimization. CoRR (2016)
Dag Sonntag, Matti Järvisalo, José M. Peña, Antti Hyttinen
Learning Optimal Chain Graphs with Answer Set Programming. UAI (2015)
Antti Hyttinen, Frederick Eberhardt, Matti Järvisalo
Do-calculus when the True Graph Is Unknown. UAI (2015)
Antti Hyttinen, Frederick Eberhardt, Matti Järvisalo
Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming. UAI (2014)
Antti Hyttinen, Patrik O. Hoyer, Frederick Eberhardt, Matti Järvisalo
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure. UAI (2013)
Antti Hyttinen, Patrik O. Hoyer, Frederick Eberhardt, Matti Järvisalo
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure. CoRR (2013)
Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer
Experiment selection for causal discovery. J. Mach. Learn. Res. 14 (1) (2013)
Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer
Noisy-OR Models with Latent Confounding CoRR (2012)
Patrik O. Hoyer, Antti Hyttinen
Bayesian Discovery of Linear Acyclic Causal Models CoRR (2012)
Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer
Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables. UAI (2012)
Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer
Learning linear cyclic causal models with latent variables. J. Mach. Learn. Res. 13 (2012)
Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer
Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables CoRR (2012)
Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer
Noisy-OR Models with Latent Confounding. UAI (2011)
Patrik O. Hoyer, Antti Hyttinen
Bayesian Discovery of Linear Acyclic Causal Models. UAI (2009)